Recent Developments and Applications of Crop Disease Detection, Prediction, and Early Warning: A review

Jingyi Yan , Huarui Wu , Zhihua Diao , Yisheng Miao , Baohua Zhang , Chunjiang Zhao

Engineering ›› : 202510032

PDF (4629KB)
Engineering ›› :202510032 DOI: 10.1016/j.eng.2025.10.032
Research
research-article
Recent Developments and Applications of Crop Disease Detection, Prediction, and Early Warning: A review
Author information +
History +
PDF (4629KB)

Abstract

Crop diseases represent a significant threat to global agricultural productivity and food security. The advancement of non-invasive and efficient crop health monitoring technologies is critical for sustainable crop protection and yield stability. The continuous progress of sensing systems and computational methodologies offers promising avenues for developing intelligent agricultural disease monitoring systems. This review systematically evaluates existing research from three dimensions: sensors and systems, methods and algorithms, and applications. It provides an in-depth analysis of the roles of different sensors and systems, discusses key methods and techniques, prediction, and early warning, and explores their applications in real-world agricultural scenarios. Furthermore, this paper identifies the main challenges in agricultural disease surveillance research, particularly in the development of real-time detection techniques, the construction of early-warning models, and the promotion of data sharing and collaboration. Finally, innovative directions and application prospects are explored for integrating crop disease monitoring with big data, artificial intelligence (AI), and the Internet of Things (IoT). These research advances are expected to open new avenues for theoretical innovation and practical applications in crop disease monitoring.

Keywords

Crop disease detection / Crop disease prediction / Crop disease early warning / Machine learning

Cite this article

Download citation ▾
Jingyi Yan, Huarui Wu, Zhihua Diao, Yisheng Miao, Baohua Zhang, Chunjiang Zhao. Recent Developments and Applications of Crop Disease Detection, Prediction, and Early Warning: A review. Engineering 202510032 DOI:10.1016/j.eng.2025.10.032

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Eruaga MA. Policy strategies for managing food safety risks associated with climate change and agriculture. Int J Sch Res Rev 2024; 04(01):021-32.

[2]

Bhattacharya S. Deadly new wheat disease threatens Europe’s crops. Nature 2017; 542(7640):145-6.

[3]

Gianessi LP, Reigner NP. The value of herbicides in US crop production. Cambridge: Cambridge University Press; 2007.

[4]

Altieri MA. Agroecology: the science of sustainable agriculture. 2nd ed. Boca Raton: CRC Press; 2018.

[5]

Nazarov PA, Baleev DN, Ivanova MI, Sokolova LM, Karakozova MV. Infectious plant diseases: etiology, current status, problems and prospects in plant protection. Acta Nat 2020; 12(3):46-59.

[6]

Dabhi MV, Koshiya DJ. Effect of abiotic factors on population dynamics of leafhopper, Amrasca Biguttula Biguttula (Ishida) in Okra. Adv Res J Crop Improv 2014; 5(1):11-4.

[7]

Wani JA, Sharma S, Muzamil M, Ahmed S, Sharma S, Singh S. Machine learning and deep learning based computational techniques in automatic agricultural diseases detection: methodologies, applications, and challenges. Arch Comput Meth Eng 2022; 29(1):641-77.

[8]

Sankaran S, Mishra A, Ehsani R, Davis C. A review of advanced techniques for detecting plant diseases. Comput Electron Agric 2010; 72(1):1-13.

[9]

Picon A, Eguskiza I, Galan P, Gomez-Zamanillo L, Romero J, Klukas C, et al. Crop-conditional semantic segmentation for efficient agricultural disease assessment. Artif Intell Agric 2025; 15(1):79-87.

[10]

Dang LM, Wang H, Li Y, Min K, Kwak JT, Lee ON, et al. Fusarium wilt of radish detection using RGB and near infrared images from unmanned aerial vehicles. Remote Sens 2020; 12(17):2863.

[11]

Rançon F, Bombrun L, Keresztes B, Germain C. Comparison of SIFT encoded and deep learning features for the classification and detection of esca disease in Bordeaux vineyards. Remote Sens 2018; 11(1):1.

[12]

Jr JB, Gasparoto MCG, Marcassa LG. Detection of mechanical and disease stresses in citrus plants by fluorescence spectroscopy. Appl Opt 2008; 47(11):1922-6.

[13]

Fu J, Liu J, Zhao R, Chen Z, Qiao Y, Li D. Maize disease detection based on spectral recovery from RGB images. Front Plant Sci 2022;13:1056842.

[14]

Zhang J, Huang Y, Pu R, Gonzalez-Moreno P, Yuan L, Wu K, et al. Monitoring plant diseases and pests through remote sensing technology: a review. Comput Electron Agric 2019;165:104943.

[15]

Hahn F. Actual pathogen detection: sensors and algorithms—a review. Algorithms 2009; 2(1):301-38.

[16]

Mahlein AK. Plant disease detection by imaging sensors—parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis 2016; 100(2):241-51.

[17]

Fenu G, Malloci FM. Forecasting plant and crop disease: an explorative study on current algorithms. Big Data Cogn Comput 2021; 5(1):2.

[18]

Zhang J, Zhang B, Chen Z, Nyalala I, Chen K, Gao J. A salient feature establishment tactic for cassava disease recognition. Artif Intell Agric 2024;14:115-32.

[19]

Fagodiya RK, Trivedi A, Fagodia BL. Impact of weather parameters on Alternaria leaf spot of soybean incited by Alternaria alternata. Sci Rep 2022; 12(1):6131.

[20]

Alam MJ, Awal MA, Mustafa MN. Crops diseases detection and solution system. Int J Electr Comput Eng IJECE 2019; 9(3):2112.

[21]

Kurmi Y, Gangwar S, Agrawal D, Kumar S, Srivastava HS. Leaf image analysis-based crop diseases classification. Signal Image Video Process 2021; 15(3):589-97.

[22]

Rumpf T, Mahlein AK, Steiner U, Oerke EC, Dehne HW, Plümer L. Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput Electron Agric 2010; 74(1):91-9.

[23]

Babu AJ, Reddy GS, Neelakantan P, Deepak S. Cotton crop disease detection using DL techniques. In: Proceedings of the 2023 IEEE International Conference on ICT in Business Industry & Government (ICTBIG); 2023 Dec 8-9; Indore, India. New York City: IEEE; 2023. p. 1-6.

[24]

Jiang P, Chen Y, Liu B, He D, Liang C. Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access 2019;7:59069-80.

[25]

Khattab A, Habib SED, Ismail H, Zayan S, Fahmy Y, Khairy MM. An IoT-based cognitive monitoring system for early plant disease forecast. Comput Electron Agric 2019;166:105028.

[26]

Padmavathi B, BhagyaLakshmi A, Vishnupriya G, Datchanamoorthy K. IoT-based prediction and classification framework for smart farming using adaptive multi-scale deep networks. Expert Syst Appl 2024;254:124318.

[27]

Kowalska A, Ashraf H. Advances in deep learning algorithms for agricultural monitoring and management. ARAIC 2023; 6(1):2855.

[28]

Delfani P, Thuraga V, Banerjee B, Chawade A. Integrative approaches in modern agriculture: IoT, ML, and AI for disease forecasting amidst climate change. Precis Agric 2024; 25(5):2589-613.

[29]

Caubel J, Launay M, Ripoche D, Gouache D, Buis S, Huard F, et al. Climate change effects on leaf rust of wheat: implementing a coupled crop-disease model in a French regional application. Eur J Agron 2017;90:53-66.

[30]

Ngugi HN, Ezugwu AE, Akinyelu AA, Abualigah L. Revolutionizing crop disease detection with computational deep learning: a comprehensive review. Environ Monit Assess 2024; 196(3):302.

[31]

Bischoff V, Farias K, Menzen JP, Pessin G. Technological support for detection and prediction of plant diseases: a systematic mapping study. Comput Electron Agric 2021;181:105922.

[32]

Wu J, Cao J, Chen J, Huang L, Wang Y, Sun C, et al. Detection and classification of volatile compounds emitted by three fungi-infected citrus fruit using gas chromatography-mass spectrometry. Food Chem 2023;412:135524.

[33]

Ali MM, Bachik NA, Muhadi N, Tuan Yusof TN, Gomes C. Non-destructive techniques of detecting plant diseases: a review. Physiol Mol Plant Pathol 2019;108:101426.

[34]

Khaled AY, Abd Aziz S, Bejo SK, Nawi NM, Seman IA, Onwude DI. Early detection of diseases in plant tissue using spectroscopy-applications and limitations. Appl Spectrosc Rev 2018; 53(1):36-64.

[35]

Su WH. Advanced machine learning in point spectroscopy, RGB- and hyperspectral-imaging for automatic discriminations of crops and weeds: a review. Smart Cities 2020; 3(3):767-92.

[36]

Chouhan SS, Singh UP, Jain S. Applications of computer vision in plant pathology: a survey. Arch Comput Meth Eng 2020; 27(2):611-32.

[37]

Dang M, Wang H, Li Y, Nguyen TH, Tightiz L, Xuan-Mung N, et al. Computer vision for plant disease recognition: a comprehensive review. Bot Rev 2024; 90(3):251-311.

[38]

Bhargava A, Shukla A, Goswami OP, Alsharif MH, Uthansakul P, Uthansakul M. Plant leaf disease detection, classification, and diagnosis using computer vision and artificial intelligence: a review. IEEE Access 2024;12:37443-69.

[39]

Das A, Pathan F, Jim JR, Kabir MM, Mridha MF. Deep learning-based classification, detection, and segmentation of tomato leaf diseases: a state-of-the-art review. Artif Intell Agric 2025; 15(2):192-220.

[40]

Cen H, Weng H, Yao J, He M, Lv J, Hua S, et al. Chlorophyll fluorescence imaging uncovers photosynthetic fingerprint of citrus Huanglongbing. Front Plant Sci 2017;8:1509.

[41]

Pinto F, Damm A, Schickling A, Panigada C, Cogliati S, Müller-Linow M, et al. Sun-induced chlorophyll fluorescence from high-resolution imaging spectroscopy data to quantify spatio-temporal patterns of photosynthetic function in crop canopies. Plant Cell Environ 2016; 39(7):1500-12.

[42]

Song X, Zhou G, Xu Z, Lv X, Wang Y. Detection of photosynthetic performance of stipa Bunagana seedlings under climatic change using chlorophyll fluorescence imaging. Front Plant Sci 2016;6:01254.

[43]

Harbinson J. Improving the accuracy of chlorophyll fluorescence measurements. Plant Cell Environ 2013; 36(10):1751-4.

[44]

Pineda M, Olejníčková J, Cséfalvay L, Barón M. Tracking viral movement in plants by means of chlorophyll fluorescence imaging. J Plant Physiol 2011; 168(17):2035-40.

[45]

Atta BM, Saleem M, Ali H, Bilal M, Fayyaz M. Application of fluorescence spectroscopy in wheat crop: early disease detection and associated molecular changes. J Fluoresc 2020; 30(4):801-10.

[46]

Bauriegel E, Herppich WB. Hyperspectral and chlorophyll fluorescence imaging for early detection of plant diseases, with special reference to Fusarium spec. infections on wheat. Agriculture 2014; 4(1):32-57.

[47]

Vadivambal R, Jayas DS. Applications of thermal imaging in agriculture and food industry—a review. Food Bioproc Tech 2011; 4(2):186-99.

[48]

Chen P, Shakhnovich EI. Thermal adaptation of viruses and bacteria. Biophys J 2010; 98(7):1109-18.

[49]

Bhakta I, Phadikar S, Majumder K, Mukherjee H, Sau A. A novel plant disease prediction model based on thermal images using modified deep convolutional neural network. Precis Agric 2023; 24(1):23-39.

[50]

Hashim IC, Shariff ARM, Bejo SK, Muharam FM, Ahmad K, Hashim H. Application of thermal imaging for plant disease detection. IOP Conf Ser: Earth Environ Sci 2020; 540(1):012052.

[51]

Baranowski P, Jedryczka M, Mazurek W, Babula-Skowronska D, Siedliska A, Kaczmarek J. Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria. PLoS One 2015; 10(3):0122913.

[52]

Martinelli F, Scalenghe R, Davino S, Panno S, Scuderi G, Ruisi P, et al. Advanced methods of plant disease detection: a review. Agron Sustain Dev 2015; 35(1):1-25.

[53]

Xavier TWF, Souto RNV, Statella T, Galbieri R, Santos ES, Suli GS, et al. Identification of ramularia leaf blight cotton disease infection levels by multispectral, multiscale UAV imagery. Drones 2019;3:33.

[54]

Dammer KH, Möller B, Rodemann B, Heppner D. Detection of head blight (Fusarium ssp.) in winter wheat by color and multispectral image analyses. Crop Prot 2011; 30(4):420-8.

[55]

Nguyen C, Sagan V, Skobalski J, Severo JI. Early detection of wheat yellow rust disease and its impact on terminal yield with multi-spectral UAV-imagery. Remote Sens 2023; 15(13):3301.

[56]

Zhang S, Li X, Ba Y, Lyu X, Zhang M, Li M. Banana Fusarium wilt disease detection by supervised and unsupervised methods from UAV-based multispectral imagery. Remote Sens 2022; 14(5):1231.

[57]

Mahlein AK, Steiner U, Hillnhütter C, Dehne HW, Oerke EC. Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods 2012; 8(1):3.

[58]

Wu G, Fang Y, Jiang Q, Cui M, Li N, Ou Y, et al. Early identification of strawberry leaves disease utilizing hyperspectral imaging combing with spectral features, multiple vegetation indices and textural features. Comput Electron Agric 2023;204:107553.

[59]

Cao Y, Yuan P, Xu H, Martínez-Ortega JF, Feng J, Zhai Z. Detecting asymptomatic infections of rice bacterial leaf blight using hyperspectral imaging and 3-dimensional convolutional neural network with spectral dilated convolution. Front Plant Sci 2022;13:963170.

[60]

Liu Z, Huang J, Tao R. Characterizing and estimating fungal disease severity of rice brown spot with hyperspectral reflectance data. Rice Sci 2008; 15(3):232-42.

[61]

Lu J, Zhou M, Gao Y, Jiang H. Using hyperspectral imaging to discriminate yellow leaf curl disease in tomato leaves. Precis Agric 2018; 19(3):379-94.

[62]

Mahlein AK, Steiner U, Dehne HW, Oerke EC. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precis Agric 2010; 11(4):413-31.

[63]

Zhu H, Chu B, Zhang C, Liu F, Jiang L, He Y. Hyperspectral imaging for presymptomatic detection of tobacco disease with successive projections algorithm and machine-learning classifiers. Sci Rep 2017; 7(1):4125.

[64]

Cen Y, Huang Y, Hu S, Zhang L, Zhang J. Early detection of bacterial wilt in tomato with portable hyperspectral spectrometer. Remote Sens 2022; 14(12):2882.

[65]

Dutta K, Talukdar D, Bora SS. Segmentation of unhealthy leaves in cruciferous crops for early disease detection using vegetative indices and OTSU thresholding of aerial images. Measurement 2022;189:110478.

[66]

Abdulridha J, Ampatzidis Y, Ehsani R, De Castro AI. Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado. Comput Electron Agric 2018;155:203-11.

[67]

Abdulridha J, Ampatzidis Y, Kakarla SC, Roberts P. Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques. Precis Agric 2020; 21(5):955-78.

[68]

Kerkech M, Hafiane A, Canals R. Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images. Comput Electron Agric 2018;155:237-43.

[69]

Mothapo MC, Dube T, Abdel-Rahman E, Sibanda M. Progress in the use of geospatial and remote sensing technologies in the assessment and monitoring of tomato crop diseases. Geocarto Int 2022; 37(16):4784-804.

[70]

Donoso A, Valenzuela S. In-field molecular diagnosis of plant pathogens: recent trends and future perspectives. Plant Pathol 2018; 67(7):1451-61.

[71]

Cesewski E, Johnson BN. Electrochemical biosensors for pathogen detection. Biosens Bioelectron 2020;159:112214.

[72]

Venbrux M, Crauwels S, Rediers H. Current and emerging trends in techniques for plant pathogen detection. Front Plant Sci 2023;14:1120968.

[73]

Kumari M, Yagnik KN, Gupta V, Singh IK, Gupta R, Verma PK, et al. Metabolomics-driven investigation of plant defense response against pest and pathogen attack. Physiol Plant 2024; 176(2):e14270.

[74]

Kumar V, Arora K. Trends in nano-inspired biosensors for plants. Mater Sci Energy Technol 2020;3:255-73.

[75]

Rana K, Mittal J, Narang J, Mishra A, Pudake RN. Graphene based electrochemical DNA biosensor for detection of false smut of rice (Ustilaginoidea virens ). Plant Pathol J 2021; 37(3):291-8.

[76]

Razali H, Awaludin N, Husin NH, Zulkepli SA, Rahman RA, Ismail MR, et al. Development of an electrochemical immunosensor strip for early detection of rice bacterial leaf blight (BLB) disease and its application on a portable device. Malays J Anal Sci 2022; 26(6):1191-204.

[77]

Wang Y, Mao H, Xu G, Zhang X, Zhang Y. A rapid detection method for fungal spores from greenhouse crops based on CMOS image sensors and diffraction fingerprint feature processing. J Fungi 2022; 8(4):374.

[78]

Yang N, Chen C, Li T, Li Z, Zou L, Zhang R, et al. Portable rice disease spores capture and detection method using diffraction fingerprints on microfluidic chip. Micromachines 2019; 10(5):289.

[79]

Zhang X, Song H, Wang Y, Hu L, Wang P, Mao H. Detection of rice fungal spores based on micro-hyperspectral and microfluidic techniques. Biosensors 2023; 13(2):278.

[80]

Gao Y, Pan X, Xu S, Liu Z, Wang J, Yu K, et al. Fluorescence-enhanced microfluidic sensor for highly sensitive in-situ detection of copper ions in lubricating oil. Mater Des 2020;191:108693.

[81]

Mi F, Hu C, Wang Y, Wang L, Peng F, Geng P, et al. Recent advancements in microfluidic chip biosensor detection of foodborne pathogenic bacteria: a review. Anal Bioanal Chem 2022; 414(9):2883-902.

[82]

Yang L, Chen W, Bi P, Tang H, Zhang F, Wang Z. Improving vegetation segmentation with shadow effects based on double input networks using polarization images. Comput Electron Agric 2022;199:107123.

[83]

Meng J, Ren W, Yu R, Wu D, Zhang R, Xie Y, et al. Contrast enhanced color polarization image fusion. Optik 2023;284:170935.

[84]

Zhao Y, Reda M, Feng K, Zhang P, Cheng G, Ren Z, et al. Detecting giant cell tumor of bone lesions using Mueller matrix polarization microscopic imaging and multi-parameters fusion network. IEEE Sens J 2020; 20(13):7208-15.

[85]

Zhang X, Bian F, Wang Y, Hu L, Yang N, Mao H. A method for capture and detection of crop airborne disease spores based on microfluidic chips and micro Raman spectroscopy. Foods 2022; 11(21):3462.

[86]

Bonah E, Huang X, Aheto JH, Osae R. Application of electronic nose as a non-invasive technique for odor fingerprinting and detection of bacterial foodborne pathogens: a review. J Food Sci Technol 2020; 57(6):1977-90.

[87]

Makarichian A, Chayjan RA, Ahmadi E, Zafari D. Early detection and classification of fungal infection in garlic (A. sativum) using electronic nose. Comput Electron Agric 2022;192:106575.

[88]

Zheng Z, Zhang C. Electronic noses based on metal oxide semiconductor sensors for detecting crop diseases and insect pests. Comput Electron Agric 2022;197:106988.

[89]

Nouri B, Fotouhi K, Mohtasebi SS, Nasiri A, Goldansaaz SH. Detection of different densities of Ephestia kuehniella pest on white flour at different larvae instar by an electronic nose system. J Stored Prod Res 2019;84:101522.

[90]

Haghbin N, Bakhshipour A, Mousanejad S, Zareiforoush H. Monitoring botrytis cinerea infection in kiwifruit using electronic nose and machine learning techniques. Food Bioproc Tech 2023; 16(4):749-67.

[91]

Cellini A, Blasioli S, Biondi E, Bertaccini A, Braschi I, Spinelli F. Potential applications and limitations of electronic nose devices for plant disease diagnosis. Sensors 2017; 17(11):2596.

[92]

Romain AC, Nicolas J. Long term stability of metal oxide-based gas sensors for e-nose environmental applications: an overview. Sens Actuators B Chem 2010; 146(2):502-6.

[93]

Wilson AD. Applications of electronic-nose technologies for noninvasive early detection of plant, animal and human diseases. Chemosensors 2018; 6(4):45.

[94]

Wang Y, An J, Shao M, Wu J, Zhou D, Yao X, et al. A comprehensive review of proximal spectral sensing devices and diagnostic equipment for field crop growth monitoring. Precis Agric 2025; 26(3):54.

[95]

Dyussembayev K, Sambasivam P, Bar I, Brownlie JC, Shiddiky MJA, Ford R. Biosensor technologies for early detection and quantification of plant pathogens. Front Chem 2021;9:636245.

[96]

Arivazhagan S, Shebiah RN, Ananthi S, Varthini SV. Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int CIGR J 2013; 15(1):211-7.

[97]

Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA. Using deep transfer learning for image-based plant disease identification. Comput Electron Agric 2020;173:105393.

[98]

Domingues T, Brandão T, Ferreira JC. Machine learning for detection and prediction of crop diseases and pests: a comprehensive survey. Agriculture 2022; 12(9):1350.

[99]

Kowalska A, Ashraf H. Advances in deep learning algorithms for agricultural monitoring and management. ARAIC 2021; 4(1):68-88.

[100]

Zahir SADM, Omar AF, Jamlos MF, Azmi MAM, Muncan J. A review of visible and near-infrared (Vis-NIR) spectroscopy application in plant stress detection. Sens Actuators A Phys 2022;338:113468.

[101]

Omia E, Bae H, Park E, Kim MS, Baek I, Kabenge I, et al. Remote sensing in field crop monitoring: a comprehensive review of sensor systems, data analyses and recent advances. Remote Sens 2023; 15(2):354.

[102]

Yang H, Zhang D, Huang W, Gao Z, Yang X, Li C, et al. Application and evaluation of wavelet-based denoising method in hyperspectral imagery data. In: Li D, Chen Y, editors. Proceedings of the 5th IFIP TC 5, SIG 5.1 International Conference, CCTA 2011; 2011 Oct 29-31; Beijing, China. Berlin: Springer; 2012. p. 461-9.

[103]

Yoon SC, Park B. Hyperspectral image processing methods. In: Park B, Lu R, editors. Hyperspectral imaging technology in food and agriculture. New York City: Springer New York; 2015. p. 81-101.

[104]

Zhao D, Feng S, Cao Y, Yu F, Guan Q, Li J, et al. Study on the classification method of rice leaf blast levels based on fusion features and adaptive-weight immune particle swarm optimization extreme learning machine algorithm. Front Plant Sci 2022;13:879668.

[105]

Zheng K, Feng T, Zhang W, Huang X, Li Z, Zhang D, et al. Variable selection by double competitive adaptive reweighted sampling for calibration transfer of near infrared spectra. Chemom Intell Lab Syst 2019;191:109-17.

[106]

Feng ZH, Wang LY, Yang ZQ, Zhang YY, Li X, Song L, et al. Hyperspectral monitoring of powdery mildew disease severity in wheat based on machine learning. Front Plant Sci 2022;13:828454.

[107]

Li X, Fu X, Li H. A CARS-SPA-GA feature wavelength selection method based on hyperspectral imaging with potato leaf disease classification. Sensors 2024; 24(20):6566.

[108]

Mahlein AK, Rumpf T, Welke P, Dehne HW, Plümer L, Steiner U, et al. Development of spectral indices for detecting and identifying plant diseases. Remote Sens Environ 2013;128:21-30.

[109]

Neupane K, Baysal-Gurel F. Automatic identification and monitoring of plant diseases using unmanned aerial vehicles: a review. Remote Sens 2021; 13(19):3841.

[110]

Brunetti A, Buongiorno D, Trotta GF, Bevilacqua V. Computer vision and deep learning techniques for pedestrian detection and tracking: a survey. Neurocomputing 2018;300:17-33.

[111]

Rani NS, Krishna AS, Sunag M, Sangamesha MA, Pushpa BR. Infield disease detection in citrus plants: integrating semantic segmentation and dynamic deep learning object detection model for enhanced agricultural yield. Neural Comput Applic 2024; 36(35):22485-510.

[112]

Zhang S, Zhang S, Zhang C, Wang X, Shi Y. Cucumber leaf disease identification with global pooling dilated convolutional neural network. Comput Electron Agric 2019;162:422-30.

[113]

Fuentes A, Yoon S, Kim S, Park D. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 2017; 17(9):2022.

[114]

Pydipati R, Burks TF, Lee WS. Identification of citrus disease using color texture features and discriminant analysis. Comput Electron Agric 2006; 52(1-2):49-59.

[115]

Qadri SAA, Huang NF, Wani TM, Bhat SA. Advances and challenges in computer vision for image-based plant disease detection: a comprehensive survey of machine and deep learning approaches. IEEE Trans Autom Sci Eng 2025;22:2639-70.

[116]

Ritharson PI, Raimond K, Mary XA, Robert JE. J. DeepRice: a deep learning and deep feature based classification of rice leaf disease subtypes. Artif Intell Agric 2024;11:34-49.

[117]

Lu Y, Wu X, Liu P, Li H, Liu W. Rice disease identification method based on improved CNN-BiGRU. Artif Intell Agric 2023;9:100-9.

[118]

Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 2016; 35(5):1285-98.

[119]

Ngugi HN, Akinyelu AA, Ezugwu AE. Machine learning and deep learning for crop disease diagnosis: performance analysis and review. Agronomy 2024; 14(12):3001.

[120]

Padol PB, Yadav AA. India. SVM classifier based grape leaf disease detection. In: Proceedings of the 2016 Conference on Advances in Signal Processing (CASP); 2016 Jun 9-11; Pune, India. New York City:IEEE; 2016. p. 175-9.

[121]

Patil P, Yaligar N, Meena SM. Comparision of performance of classifiers-SVM, RF and ANN in potato blight disease detection using leaf images. In: Proceedings of the 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC); 2017 Dec 14-16; Coimbatore, India. New York City:IEEE; 2017. p. 1-5.

[122]

Vaishnnave MP, Devi KS, Srinivasan P, Jothi GAP. Detection and classification of groundnut leaf diseases using KNN classifier. In: Proceedings of the 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN); 2019 Mar 29-30; Pondicherry, India. New York City:IEEE; 2019. p. 1-5.

[123]

Goldstein M, Uchida S. A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PLoS One 2016; 11(4):e0152173.

[124]

Munir M, Siddiqui SA, Dengel A, Ahmed S. DeepAnT: a deep learning approach for unsupervised anomaly detection in time series. IEEE Access 2019;7:1991-2005.

[125]

Botchkarev A. A new typology design of performance metrics to measure errors in machine learning regression algorithms. Interdiscip J Inf Knowl Manag 2019;14:045-76.

[126]

Abbas A, Zhang Z, Zheng H, Alami MM, Alrefaei AF, Abbas Q, et al. Drones in plant disease assessment, efficient monitoring, and detection: a way forward to smart agriculture. Agronomy 2023; 13(6):1524.

[127]

Gibbs JA, Burgess AJ, Pound MP, Pridmore TP, Murchie EH. Recovering wind-induced plant motion in dense field environments via deep learning and multiple object tracking. Plant Physiol 2019; 181(1):28-42.

[128]

Medvedieva K, Tosi T, Barbierato E, Gatti A. Balancing the scale: data augmentation techniques for improved supervised learning in cyberattack detection. Engineering 2024; 5(3):2170-205.

[129]

Sambasivam G, Opiyo GD. A predictive machine learning application in agriculture: cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egypt Inform J 2021; 22(1):27-34.

[130]

A I, R S. Multi-modal deep learning for leaf disease detection:integrating visual and non-visual data sources. In: Proceedings of the 2023 6th International Conference on Recent Trends in Advance Computing (ICRTAC); 2023 Dec 14-15; Chennai, India. New York City: IEEE; 2023. p. 186-91.

[131]

Mavromatis I, Katsaros K, Khan A. Computing within limits: an empirical study of energy consumption in ML training and inference. arXiv:2406.14328.

[132]

Hatuwal BK, Shakya A, Joshi B. Plant leaf disease recognition using random forest, KNN, SVM and CNN. Polibits 2021;62:13-9.

[133]

Bouguettaya A, Zarzour H, Kechida A, Taberkit AM. A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images. Cluster Comput 2023; 26(2):1297-317.

[134]

Bedi P, Gole P. Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network. Artif Intell Agric 2021;5:90-101.

[135]

Paymode AS, Malode VB. Transfer learning for multi-crop leaf disease image classification using convolutional neural network VGG. Artif Intell Agric 2022;6:23-33.

[136]

Raouhi EM, Lachgar M, Hrimech H, Kartit A. Optimization techniques in deep convolutional neuronal networks applied to olive diseases classification. Artif Intell Agric 2022;6:77-89.

[137]

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521(7553):436-44.

[138]

Dai G, Hu L, Fan J. DA-ActNN-YOLOV5: hybrid YOLO v5 model with data augmentation and activation of compression mechanism for potato disease identification. Comput Intell Neurosci 2022;2022:1-16.

[139]

Dai G, Fan J. An industrial-grade solution for crop disease image detection tasks. Front Plant Sci 2022;13:921057.

[140]

Lu X, Yang R, Zhou J, Jiao J, Liu F, Liu Y, et al. A hybrid model of ghost-convolution enlightened transformer for effective diagnosis of grape leaf disease and pest. J King Saud Univ Comput Inf Sci 2022; 34(5):1755-67.

[141]

Pawara P, Okafor E, Schomaker L, Wiering M. Data Augmentation for Plant Classification. In: Blanc-Talon J, Penne R, Philips W, Popescu D, Scheunders P. editors. Advanced concepts for intelligent vision systems. ACIVS 2017. Lecture Notes in Computer Science, vol 10617. Cham: Springer Cham; 2017.

[142]

Senthil Pandi S, Senthilselvi A, Gitanjali J, ArivuSelvan K, Gopal J, Vellingiri J. Rice plant disease classification using dilated convolutional neural network with global average pooling. Ecol Model 2022;474:110166.

[143]

Cardellicchio A, Solimani F, Dimauro G, Petrozza A, Summerer S, Cellini F, et al. Detection of tomato plant phenotyping traits using YOLOv5-based single stage detectors. Comput Electron Agric 2023;207:107757.

[144]

Badgujar CM, Poulose A, Gan H. Agricultural object detection with you look only once (YOLO) algorithm: a bibliometric and systematic literature review. Comput Electron Agric 2024;223:109090.

[145]

Chen P, Xiao Q, Zhang J, Xie C, Wang B. Occurrence prediction of cotton pests and diseases by bidirectional long short-term memory networks with climate and atmosphere circulation. Comput Electron Agric 2020;176:105612.

[146]

Mohameth F, Bingcai C, Sada KA. Plant disease detection with deep learning and feature extraction using plant village. J Comput Commun 2020; 08(6):10-22.

[147]

Moupojou E, Tagne A, Retraint F, Tadonkemwa A, Wilfried D, Tapamo H, et al. FieldPlant: a dataset of field plant images for plant disease detection and classification with deep learning. IEEE Access 2023;11:35398-410.

[148]

Singh D, Jain N, Jain P, Kayal P, Kumawat S, Batra N. PlantDoc:a dataset for visual plant disease detection. In: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD; 2020 Jan 5-7; Hyderabad, India. New York City: Association for Computing Machinery (ACM); 2020. p. 249-53.

[149]

Kim S, Lee M, Shin C. IoT-based strawberry disease prediction system for smart farming. Sensors 2018; 18(11):4051.

[150]

Ouhami M, Hafiane A, Es-Saady Y, El Hajji M, Canals R. Computer vision, IoT and data fusion for crop disease detection using machine learning: a survey and ongoing research. Remote Sens 2021; 13(13):2486.

[151]

Hossam M, Kamal M, Moawad M, Maher M, Salah M, Abady Y, et al. PLANTAE:an IoT-based predictive platform for precision agriculture. In: Proceedings of the 2018 International Japan-Africa Conference on Electronics, Communications and Computations (JAC-ECC); 2018 Dec 17-19; Alexandria, Egypt. New York City: IEEE; 2018. p. 87-90.

[152]

Ibrahim H, Mostafa N, Halawa H, Elsalamouny M, Daoud R, Amer H, et al. A layered IoT architecture for greenhouse monitoring and remote control. SN Appl Sci 2019; 1(3):223.

[153]

Khattab A, Abdelgawad A, Yelmarthi K. Design and implementation of a cloud-based IoT scheme for precision agriculture. In: Proceedings of the 2016 28th International Conference on Microelectronics (ICM); 2016 Dec 17-20; Giza, Egypt. New York City:IEEE; 2016. p. 201-4.

[154]

Orchi H, Sadik M, Khaldoun M. On using artificial intelligence and the Internet of Things for crop disease detection: a contemporary survey. Agriculture 2021; 12(1):9.

[155]

Krishna MMS. Plant disease detection and pesticide spraying using dip and IoT. Int J Emerg Technol Innov Res 2019; 6(4):54-8.

[156]

Fountas S, Carli G, Sørensen CG, Tsiropoulos Z, Cavalaris C, Vatsanidou A, et al. Farm management information systems: current situation and future perspectives. Comput Electron Agric 2015;115:40-50.

[157]

De Wolf ED, Isard SA. Disease cycle approach to plant disease prediction. Annu Rev Phytopathol 2007; 45(1):203-20.

[158]

Shah DA, Paul PA, De Wolf ED, Madden LV. Predicting plant disease epidemics from functionally represented weather series. Philos Trans R Soc Lond B Biol Sci 2019; 374(1775):20180273.

[159]

Neetoo H, Chuttur Y, Nazurally A, Takooree S, Mamode Ally N. Crop disease prediction using multiple linear regression modelling. In: Patel KK, Doctor G, Patel A, Lingras P, editors. Soft computing and its engineering applications. icSoftComp 2021. Communications in computer and information science. Cham: Springer International Publishing; 2022. p. 312-26.

[160]

Bi C, Chen G. Bayesian networks modeling for crop diseases. In: Li D, Liu Y, Chen Y, editors. Computer and computing technologies in agriculture IV. CCTA 2010. IFIP advances in information and communication technology. Berlin: Springer; 2011. p. 312-20.

[161]

Lo Iacono G, Van Den Bosch F, Gilligan CA. Durable resistance to crop pathogens: an epidemiological framework to predict risk under uncertainty. PLoS Comput Biol 2013; 9(1):e1002870.

[162]

Dey N, Ashour AS, Borra S, editors. Classification in BioApps-automation of decision making. Cham: Springer International Publishing; 2018.

[163]

Attri I, Awasthi LK, Sharma TP. Machine learning in agriculture: a review of crop management applications. Multimedia Tools Appl 2023; 83(5):12875-915.

[164]

Sharma K, Shivandu SK. Integrating artificial intelligence and Internet of Things (IoT) for enhanced crop monitoring and management in precision agriculture. Sens Int 2024;5:100292.

[165]

Lv M, Su WH. YOLOV5-CBAM-C3TR: an optimized model based on transformer module and attention mechanism for apple leaf disease detection. Front Plant Sci 2024;14:1323301.

[166]

Khan AI, Quadri SMK, Banday S, Latief SaJ. Deep diagnosis: a real-time apple leaf disease detection system based on deep learning. Comput Electron Agric 2022;198:107093.

[167]

Wspanialy P, Moussa M. A detection and severity estimation system for generic diseases of tomato greenhouse plants. Comput Electron Agric 2020;178:105701.

[168]

Kaur P, Harnal S, Gautam V, Singh MP, Singh SP. An approach for characterization of infected area in tomato leaf disease based on deep learning and object detection technique. Eng Appl Artif Intel 2022;115:105210.

[169]

Deng Y, Xi H, Zhou G, Chen A, Wang Y, Li L, et al. An effective image-based tomato leaf disease segmentation method using MC-UNet. Plant Phenomics 2023;5:0049.

[170]

Hassanien AE, Gaber T, Mokhtar U, Hefny H. An improved moth flame optimization algorithm based on rough sets for tomato diseases detection. Comput Electron Agric 2017;136:86-96.

[171]

Qi J, Liu X, Liu K, Xu F, Guo H, Tian X, et al. An improved YOLOv5 model based on visual attention mechanism: application to recognition of tomato virus disease. Comput Electron Agric 2022;194:106780.

[172]

Zhang D, Huang Y, Wu C, Ma M. Detecting tomato disease types and degrees using multi-branch and destruction learning. Comput Electron Agric 2023;213:108244.

[173]

Temniranrat P, Kiratiratanapruk K, Kitvimonrat A, Sinthupinyo W, Patarapuwadol S. A system for automatic rice disease detection from rice paddy images serviced via a Chatbot. Comput Electron Agric 2021;185:106156.

[174]

Sudhesh K M, Sowmya V, Sainamole Kurian P, et al. AI based rice leaf disease identification enhanced by Dynamic Mode Decomposition[J]. Engineering Applications of Artificial Intelligence 2023;120:105836.

[175]

Radhakrishnan S. An improved machine learning algorithm for predicting blast disease in paddy crop. Mater Today Proc 2020;33:682-6.

[176]

Jiang F, Lu Y, Chen Y, Cai D, Li G. Image recognition of four rice leaf diseases based on deep learning and support vector machine. Comput Electron Agric 2020;179:105824.

[177]

Oishi Y, Habaragamuwa H, Zhang Y, Sugiura R, Asano K, Akai K, et al. Automated abnormal potato plant detection system using deep learning models and portable video cameras. Int J Appl Earth Obs Geoinf 2021;104:102509.

[178]

Bienkowski D, Aitkenhead MJ, Lees AK, Gallagher C, Neilson R. Detection and differentiation between potato (Solanum tuberosum) diseases using calibration models trained with non-imaging spectrometry data. Comput Electron Agric 2019;167:105056.

[179]

Ma J, Du K, Zheng F, Zhang L, Gong Z, Sun Z. A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput Electron Agric 2018;154:18-24.

[180]

Lu J, Hu J, Zhao G, Mei F, Zhang C. An in-field automatic wheat disease diagnosis system. Comput Electron Agric 2017;142:369-79.

[181]

Johannes A, Picon A, Alvarez-Gila A, Echazarra J, Rodriguez-Vaamonde S, Navajas AD, et al. Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Comput Electron Agric 2017;138:200-9.

[182]

Kanna GP, Kumar SJKJ, Kumar Y, Changela A, Woźniak M, Shafi J, et al. Advanced deep learning techniques for early disease prediction in cauliflower plants. Sci Rep 2023; 13(1):18475.

[183]

Waheed A, Goyal M, Gupta D, Khanna A, Hassanien AE, Pandey HM. An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Comput Electron Agric 2020;175:105456.

[184]

Oberti R, Marchi M, Tirelli P, Calcante A, Iriti M, Borghese AN. Automatic detection of powdery mildew on grapevine leaves by image analysis: optimal view-angle range to increase the sensitivity. Comput Electron Agric 2014;104:1-8.

[185]

Sharif M, Khan MA, Iqbal Z, Azam MF, Lali MIU, Javed MY. Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Comput Electron Agric 2018;150:220-34.

[186]

Theerthagiri P, Ruby AU, Chandran JGC, Sardar TH, Shafeeq B. Deep SqueezeNet learning model for diagnosis and prediction of maize leaf diseases. J Big Data 2024;11:112.

[187]

Moshou D, Bravo C, Oberti R, West JS, Ramon H, Vougioukas S, et al. Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops. Biosyst Eng 2011; 108(4):311-21.

[188]

Tewari VK, Pareek CM, Lal G, Dhruw LK, Singh N. Image processing based real-time variable-rate chemical spraying system for disease control in paddy crop. Artif Intell Agric 2020;4:21-30.

[189]

Rogers PM, Stevenson WR. Weather-based fungicide spray programs for control of two foliar diseases on carrot cultivars differing in susceptibility. Plant Dis 2006; 90(3):358-64.

[190]

Latif G, Alghazo J, Maheswar R, Vijayakumar V, Butt M. Deep learning based intelligence cognitive vision drone for automatic plant diseases identification and spraying. J Intell Fuzzy Syst 2020; 39(6):8103-14.

[191]

Oberti R, Marchi M, Tirelli P, Calcante A, Iriti M, Tona E, et al. Selective spraying of grapevines for disease control using a modular agricultural robot. Biosyst Eng 2016;146:203-15.

[192]

Kundu N, Rani G, Dhaka VS, Gupta K, Nayaka SC, Vocaturo E, et al. Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning. Artif Intell Agric 2022;6:276-91.

[193]

Varun Kumar B, Gopi Krishna Rao PV. An effective hybrid attention model for crop yield prediction using IoT-based three-phase prediction with an improved sailfish optimizer. Expert Syst Appl 2024;255:124740.

[194]

Lee S, Jeong Y, Son S, Lee B. A self-predictable crop yield platform (SCYP) based on crop diseases using deep learning. Sustainability 2019; 11(13):3637.

[195]

Mahlein AK. Plant disease detection by imaging sensors-parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis 2016; 100(2):241-51.

[196]

Khan N, Ray RL, Sargani GR, Ihtisham M, Khayyam M, Ismail S. Current progress and future prospects of agriculture technology: gateway to sustainable agriculture. Sustainability 2021; 13(9):4883.

[197]

Islam S, Reza MN, Samsuzzaman S, Ahmed S, Cho YJ, Noh DH, et al. Machine vision and artificial intelligence for plant growth stress detection and monitoring: a review. Precis Agric Sci Technol 2024; 6(1):33-57.

[198]

Xiao D, Pan Y, Feng J, Yin J, Liu Y, He L. Remote sensing detection algorithm for apple fire blight based on UAV multispectral image. Comput Electron Agric 2022;199:107137.

[199]

Johnson J, Sharma G, Srinivasan S, Masakapalli SK, Sharma S, Sharma J, et al. Enhanced field-based detection of potato blight in complex backgrounds using deep learning. Plant Phenomics 2021;2021:9835724.

[200]

Sun H, Xue J, Song Y, Wang P, Wen Y, Zhang T. Detection of fruit tree diseases in natural environments: a novel approach based on stereo camera and deep learning. Eng Appl Artif Intel 2024;137:109148.

[201]

Zhang F, Bao R, Yan B, Wang M, Zhang Y, Fu S. LSANNet: a lightweight convolutional neural network for maize leaf disease identification. Biosyst Eng 2024;248:97-107.

[202]

Mahmud MS, He L, Zahid A, Heinemann P, Choi D, Krawczyk G, et al. Detection and infected area segmentation of apple fire blight using image processing and deep transfer learning for site-specific management. Comput Electron Agric 2023;209:107862.

[203]

Van De Vijver R, Mertens K, Heungens K, Somers B, Nuyttens D, Borra-Serrano I, et al. In-field detection of Alternaria solani in potato crops using hyperspectral imaging. Comput Electron Agric 2020;168:105106.

[204]

Wang X, Zhang M, Zhu J, Geng S. Spectral prediction of Phytophthora infestans infection on tomatoes using artificial neural network (ANN). Int J Remote Sens 2008; 29(6):1693-706.

[205]

Yuan L, Huang Y, Loraamm RW, Nie C, Wang J, Zhang J. Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects. Field Crops Res 2014;156:199-207.

[206]

Zhang M, Qin Z. Spectral analysis of tomato late blight infections for remote sensing of tomato disease stress in California. In: Proceedings of the IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium; 2004 Sep 20-24; Anchorage, AK, USA. New York City: IEEE; 2004. p. 4091-4.

[207]

Ray SS, Jain N, Arora RK, Chavan S, Panigrahy S. Utility of hyperspectral data for potato late blight disease detection. Photonirvachak 2011; 39(2):161-9.

[208]

Luvisi A, Ampatzidis Y, De Bellis L. Plant pathology and information technology: opportunity for management of disease outbreak and applications in regulation frameworks. Sustainability 2016; 8(8):831.

[209]

MacDonald SL, Staid M, Staid M, Cooper ML. Remote hyperspectral imaging of grapevine leafroll-associated virus 3 in cabernet sauvignon vineyards. Comput Electron Agric 2016;130:109-17.

[210]

Su J, Liu C, Coombes M, Hu X, Wang C, Xu X, et al. Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery. Comput Electron Agric 2018;155:157-66.

[211]

Santoso H, Gunawan T, Jatmiko RH, Darmosarkoro W, Minasny B. Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery. Precis Agric 2011; 12(2):233-48.

[212]

Yuan L, Bao Z, Zhang H, Zhang Y, Liang X. Habitat monitoring to evaluate crop disease and pest distributions based on multi-source satellite remote sensing imagery. Optik (Stuttg) 2017;145:66-73.

[213]

Lochan K, Khan A, Elsayed I, Suthar B, Seneviratne L, Hussain I. Advancements in precision spraying of agricultural robots: a comprehensive review. IEEE Access 2024;12:129447-83.

[214]

Yu S, Liu X, Tan Q, Wang Z, Zhang B. Sensors, systems and algorithms of 3D reconstruction for smart agriculture and precision farming: a review. Comput Electron Agric 2024;224:109229.

[215]

Terada K, Fujinami K. Improving disease forecast on different farms using sensing agricultural robot with XAI. In: Proceedings of the 2024 IEEE 13th Global Conference on Consumer Electronics (GCCE); 2024 Oct 29-Nov 1; Kitakyushu, Japan. New York City:IEEE; 2024. p. 398-402.

[216]

Bontsema J, Best S, Baur J, Ringdahl O, Oberti R, Evain S, et al. CROPS: clever robots for crops. Eng Technol Ref 2015;2015:0015.

[217]

Esau TJ, Zaman Q. Prototype variable rate sprayer for spot-application of agrochemicals in wild blueberry. Appl Eng Agric 2014; 30(5):717-25.

[218]

Samseemoung G, Soni P, Suwan P. Development of a variable rate chemical sprayer for monitoring diseases and pests infestation in coconut plantations. Agriculture 2017; 7(10):89.

[219]

Liu T, Zhang B, Tan Q, Zhou J, Yu S, Zhu Q, et al. Immersive human-machine teleoperation framework for precision agriculture: integrating UAV-based digital mapping and virtual reality control. Comput Electron Agric 2024;226:109444.

[220]

Luo H, Niu Y, Zhu M, Hu X, Ma H. Optimization of pesticide spraying tasks via multi-UAVs using genetic algorithm. Math Probl Eng 2017; 2017(1):7139157.

[221]

Oerke EC, Dehne HW. Safeguarding production—losses in major crops and the role of crop protection. Crop Prot 2004; 23(4):275-85.

[222]

Verreet JA, Klink H, Hoffmann GM. Regional monitoring for disease prediction and optimization of plant protection measuares: the IPM wheat model. Plant Dis 2000; 84(8):816-26.

[223]

Rashid M, Bari BS, Yusup Y, Kamaruddin MA, Khan N. A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction. IEEE Access 2021;9:63406-39.

[224]

John MA, Bankole I, Ajayi-Moses O, Ijila T, Jeje T, Lalit P. Relevance of advanced plant disease detection techniques in disease and pest management for ensuring food security and their implication: a review. Am J Plant Sci 2023; 14(11):1260-95.

[225]

Boyd LA, Ridout C, O’Sullivan DM, Leach JE, Leung H. Plant-pathogen interactions: disease resistance in modern agriculture. Trends Genet 2013; 29(4):233-40.

[226]

Li L, Zhang Q, Huang D. A review of imaging techniques for plant phenotyping. Sensors 2014; 14(11):20078-111.

[227]

Yang G, Liu J, Zhao C, Li Z, Huang Y, Yu H, et al. Unmanned aerial vehicle remote sensing for field-based crop phenotyping: current status and perspectives. Front Plant Sci 2017;8:1111.

[228]

Lammerts van Bueren ET, Jones SS, Tamm L, Murphy KM, Myers JR, Leifert C, et al. The need to breed crop varieties suitable for organic farming, using wheat, tomato and broccoli as examples: a review. NJAS Wagening. J Life Sci 2011; 58(3-4):193-205.

[229]

Zheng C, Abd-Elrahman A, Whitaker V. Remote sensing and machine learning in crop phenotyping and management, with an emphasis on applications in strawberry farming. Remote Sens 2021; 13(3):531.

[230]

Prithiviraj K. A novel approaches to crop variety development for sustainable agriculture using hybrid machine learning model. J Electr Syst 2024; 20(6S):2811-20.

[231]

Stuthman DD, Leonard KJ, Miller-Garvin J. Breeding crops for durable resistance to disease. Adv Agron 2007;95:319-67.

[232]

Buja I, Sabella E, Monteduro AG, Chiriacò MS, De Bellis L, Luvisi A, et al. Advances in plant disease detection and monitoring: from traditional assays to in-field diagnostics. Sensors 2021; 21(6):2129.

[233]

Grishina A, Sherstneva O, Mysyagin S, Brilkina A, Vodeneev V. Detecting plant infections: prospects for chlorophyll fluorescence imaging. Agronomy 2024; 14(11):2600.

[234]

Conrad AO, Li W, Lee DY, Wang GL, Rodriguez-Saona L, Bonello P. Machine learning-based presymptomatic detection of rice sheath blight using spectral profiles. Plant Phenomics 2020;2020:8954085.

[235]

Li D, Zhu B, Pang K, Zhang Q, Qu M, Liu W, et al. Virtual sensor array based on piezoelectric cantilever resonator for identification of volatile organic compounds. ACS Sens 2022; 7(5):1555-63.

[236]

Wang H, Qian X, Zhang L, Xu S, Li H, Xia X, et al. A method of high throughput monitoring crop physiology using chlorophyll fluorescence and multispectral imaging. Front Plant Sci 2018;9:407.

[237]

Zhou S, Zhou J, Pan Y, Wu Q, Ping J. Wearable electrochemical sensors for plant small-molecule detection. Trends Plant Sci 2024; 29(2):219-31.

[238]

Shuaibu M, Lee WS, Schueller J, Gader P, Hong YK, Kim S. Unsupervised hyperspectral band selection for apple Marssonina blotch detection. Comput Electron Agric 2018;148:45-53.

[239]

Cui R, Li J, Wang Y, Fang S, Yu K, Zhao Y. Hyperspectral imaging coupled with Dual-channel convolutional neural network for early detection of apple valsa canker. Comput Electron Agric 2022;202:107411.

[240]

Tian L, Xue B, Wang Z, Li D, Yao X, Cao Q, et al. Spectroscopic detection of rice leaf blast infection from asymptomatic to mild stages with integrated machine learning and feature selection. Remote Sens Environ 2021;257:112350.

[241]

Yeh YH, Chung WC, Liao JY, Chung CL, Kuo YF, Lin TT. Strawberry foliar anthracnose assessment by hyperspectral imaging. Comput Electron Agric 2016;122:1-9.

[242]

Cabrera Ardila CE, Alberto Ramirez L, Prieto Ortiz FA. Spectral analysis for the early detection of anthracnose in fruits of Sugar Mango (Mangifera indica). Comput Electron Agric 2020;173:105357.

[243]

Duarte-Carvajalino JM, Alzate DF, Ramirez AA, Santa-Sepulveda JD, Fajardo-Rojas AE, Soto-Suárez M. Evaluating late blight severity in potato crops using unmanned aerial vehicles and machine learning algorithms. Remote Sens 2018; 10(10):1513.

[244]

Kini AS, Prema KV, Pai SN. Early stage black pepper leaf disease prediction based on transfer learning using ConvNets. Sci Rep 2024; 14(1):1404.

[245]

Xie C, Lee WS. Detection of citrus black spot symptoms using spectral reflectance. Postharvest Biol Technol 2021;180:111627.

[246]

Brilli F, Loreto F, Baccelli I. Exploiting plant volatile organic compounds (VOCs) in agriculture to improve sustainable defense strategies and productivity of crops. Front Plant Sci 2019;10:264.

[247]

Aktas D, Ortlek BE, Civas M, Baradari E, Kilic AB, Bilgen FE, et al. Odor-based molecular communications: state-of-the-art, vision, challenges, and frontier directions. IEEE Commun Surv Tutor 2025; 27(4):2658-92.

[248]

Jacquemoud S, Ustin SL. Leaf optical properties:a state of the art. In: Proceedings of the 8th International Symposium Physical Measurements & Signatures in Remote Sensing; 2002 Jan 8-12; Aussois, France. Paris: CNES; 2002. p. 223-32.

[249]

Lee WS, Alchanatis V, Yang C, Hirafuji M, Moshou D, Li C. Sensing technologies for precision specialty crop production. Comput Electron Agric 2010; 74(1):2-33.

[250]

Golhani K, Balasundram SK, Vadamalai G, Pradhan B. A review of neural networks in plant disease detection using hyperspectral data. Inf Process Agric 2018; 5(3):354-71.

[251]

Kumari K, Parray R, Basavaraj YB, Godara S, Mani I, Kumar R, et al. Spectral sensor-based device for real-time detection and severity estimation of groundnut bud necrosis virus in tomato. J Field Robot 2025; 42(1):5-19.

[252]

Yang S, Li C, Li X, Jiang J, Zhao Y, Wang X, et al. Early detection of fungal infection in citrus using biospeckle imaging. Comput Electron Agric 2024;225:109293.

[253]

Sun Y, Zheng Y. Prediction of tomato plants infected by fungal pathogens at different disease severities using e-nose and GC-MS. J Plant Dis Prot 2024; 131(3):835-46.

[254]

Li C, Krewer GW, Ji P, Scherm H, Kays SJ. Gas sensor array for blueberry fruit disease detection and classification. Postharvest Biol Technol 2010; 55(3):144-9.

[255]

Gent DH, Mahaffee WF, McRoberts N, Pfender WF. The use and role of predictive systems in disease management. Annu Rev Phytopathol 2013; 51(1):267-89.

[256]

Nabi F, Jamwal S, Padmanbh K. Wireless sensor network in precision farming for forecasting and monitoring of apple disease: a survey. Int J Inf Technol 2022; 14(2):769-80.

[257]

Boursianis AD, Papadopoulou MS, Diamantoulakis P, Liopa-Tsakalidi A, Barouchas P, Salahas G, et al. Internet of Things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: a comprehensive review. Internet Things 2022;18:100187.

[258]

Weiss M, Jacob F, Duveiller G. Remote sensing for agricultural applications: a meta-review. Remote Sens Environ 2020;236:111402.

[259]

Mekala MS, Viswanathan PA. A survey:smart agriculture IoT with cloud computing. In: Proceedings of the 2017 International Conference on Microelectronic Devices, Circuits and Systems (ICMDCS); 2017 Aug 10-12; Vellore, India. New York City: IEEE; 2017. p. 1-7.

[260]

Gilligan CA. Developing predictive models and early warning systems for invading pathogens: wheat rusts. Annu Rev Phytopathol 2024; 62(1):217-41.

[261]

Wang Y, Zhang X, Taha MF, Chen T, Yang N, Zhang J, et al. Detection method of fungal spores based on fingerprint characteristics of diffraction-polarization images. J Fungi 2023; 9(12):1131.

[262]

Hendricks KE, Christman MC, Roberts PD. The effect of weather and location of fruit within the tree on the incidence and severity of citrus black spot on fruit. Sci Rep 2020; 10(1):1389.

[263]

Xu C, Zhao L, Wen H, Zhang L. Spatial-temporal analysis and trend prediction of regional crop disease based on electronic medical records. Appl Soft Comput 2024;167:112423.

[264]

Malicdem AR. Rice blast disease forecasting for Northern Philippines. Trans Inf Sci Appl 2015;12:120-9.

[265]

Sengupta S, Das AK. Particle swarm optimization based incremental classifier design for rice disease prediction. Comput Electron Agric 2017;140:443-51.

[266]

Dai G, Fan J, Tian Z, Wang C. PPLC-Net: neural network-based plant disease identification model supported by weather data augmentation and multi-level attention mechanism. J King Saud Univ Comput Inf Sci 2023; 35(5):101555.

[267]

Zhang J, Pu R, Yuan L, Huang W, Nie C, Yang G. Integrating remotely sensed and meteorological observations to forecast wheat powdery mildew at a regional scale. IEEE J Sel Top Appl Earth Obs Remote Sens 2014; 7(11):4328-39.

[268]

Li W, Liu Y, Chen H, Zhang CC. Estimation model of winter wheat disease based on meteorological factors and spectral information. Food Prod Process Nutr 2020; 2(1):5.

[269]

Dutta S, Singh SK, Khullar M. A case study on forewarning of yellow rust affected areas on wheat crop using satellite data. Photonirvachak 2014; 42(2):335-42.

[270]

Mentlak TA, Kombrink A, Shinya T, Ryder LS, Otomo I, Saitoh H, et al. Effector-mediated suppression of chitin-triggered immunity by Magnaporthe oryzae is necessary for rice blast disease. Plant Cell 2012; 24(1):322-35.

[271]

Fall ML, Carisse O. Dynamic simulation for predicting warning and action thresholds: a novelty for strawberry powdery mildew management. Agric For Meteorol 2022;312:108711.

[272]

Peterson T, Spitsbergen J, Feist S, Kent M. Luna stain, an improved selective stain for detection of microsporidian spores in histologic sections. Dis Aquat Organ 2011; 95(2):175-80.

[273]

Yang N, Yu J, Wang A, Tang J, Zhang R, Xie L, et al. A rapid rice blast detection and identification method based on crop disease spores’ diffraction fingerprint texture. J Sci Food Agric 2020; 100(9):3608-21.

[274]

Wang Y, Du X, Ma G, Liu Y, Wang B, Mao H. Classification methods for airborne disease spores from greenhouse crops based on multifeature fusion. Appl Sci 2020; 10(21):7850.

[275]

Parthasarathy S. Fundamentals of plant pathology. London: CRC Press; 2024.

[276]

Mathur M, Mathur P. Predictive ecological niche modelling of an important bio-control agent: Trichoderma harzianum (Rifai) using the MaxEnt machine learning tools with climatic and non-climatic predictors. Biocontrol Sci Tech 2023; 33(9):820-54.

[277]

Newlands NK. Model-based forecasting of agricultural crop disease risk at the regional scale, integrating airborne inoculum, environmental, and satellite-based monitoring data. Front Environ Sci 2018;6:63.

[278]

Vc SS, Ah S, Albaaji GF. Precision farming for sustainability: an agricultural intelligence model. Comput Electron Agric 2024;226:109386.

[279]

Lu W, Newlands NK, Carisse O, Atkinson DE, Cannon AJ. Disease risk forecasting with Bayesian learning networks: application to grape powdery mildew (Erysiphe necator) in vineyards. Agronomy 2020; 10(5):622.

[280]

Liu K, Zhang C, Yang X, Diao M, Liu H, Li M. Development of an occurrence prediction model for cucumber downy mildew in solar greenhouses based on long short-term memory neural network. Agronomy 2022; 12(2):442.

[281]

Metcalf CJE, Walter KS, Wesolowski A, Buckee CO, Shevliakova E, Tatem AJ, et al. Identifying climate drivers of infectious disease dynamics: recent advances and challenges ahead. Proc Biol Sci 1860; 2017(284):20170901.

[282]

Caubel J, Launay M, Lannou C, Brisson N. Generic response functions to simulate climate-based processes in models for the development of airborne fungal crop pathogens. Ecol Model 2012;242:92-104.

[283]

Xu C, Ding J, Qiao Y, Zhang L. Tomato disease and pest diagnosis method based on the stacking of prescription data. Comput Electron Agric 2022;197:106997.

[284]

Wongchai A, Jenjeti D, Priyadarsini AI, Deb N, Bhardwaj A, Tomar P. Farm monitoring and disease prediction by classification based on deep learning architectures in sustainable agriculture. Ecol Model 2022;474:110167.

[285]

Udutalapally V, Mohanty SP, Pallagani V, Khandelwal V. sCrop: a novel device for sustainable automatic disease prediction, crop selection, and irrigation in Internet-of-Agro-Things for smart agriculture. IEEE Sens J 2021; 21(16):17525-38.

[286]

Arowolo ME, Aaron WC, Kugbiyi AO, Eteng US, Iloh D, Aguma CP, et al. Integrating AI enhanced remote sensing technologies with IoT networks for precision environmental monitoring and predicative ecosystem management. World J Adv Res Rev 2024; 23(2):2156-66.

[287]

Al-Otaibi S, Khan R, Ali J, Ahmed A. Artificial intelligence and Internet of Things-enabled decision support system for the prediction of bacterial stalk root disease in maize crop. Comput Intell 2024; 40(1):e12632.

[288]

Wang S, Xu D, Liang H, Bai Y, Li X, Zhou J, et al. Advances in deep learning applications for plant disease and pest detection: a review. Remote Sens 2025; 17(4):698.

[289]

Liu J, He C, Jiang Y, Wang M, Ye Z, He M. A high-precision identification method for maize leaf diseases and pests based on LFMNet under complex backgrounds. Plants 2024; 13(13):1827.

[290]

Elijah O, Rahman TA, Orikumhi I, Leow CY, Hindia MHDN. An overview of Internet of Things (IoT) and data analytics in agriculture: benefits and challenges. IEEE Internet Things J 2018; 5(5):3758-73.

[291]

Luo X, Xiong S, Jia X, Zeng Y, Chen X. AIoT-enabled data management for smart agriculture: a comprehensive review on emerging technologies. IEEE Access 2025;13:102964-93.

[292]

Chamara N, Islam MD, Bai G, Shi Y, Ge Y. Ag-IoT for crop and environment monitoring: past, present, and future. Agr Syst 2022;203:103497.

[293]

González-Rodríguez VE, Izquierdo-Bueno I, Cantoral JM, Carbú M, Garrido C. Artificial intelligence: a promising tool for application in phytopathology. Horticulturae 2024; 10(3):197.

PDF (4629KB)

0

Accesses

0

Citation

Detail

Sections
Recommended

/