Agriculture is the backbone of global food security, yet plant diseases significantly impact crop yields and farmers' livelihoods. Traditional disease detection methods are often time-consuming and require expert intervention, making them less feasible for large-scale farming. Recent advancements in deep learning have revolutionized plant disease identification by offering automated, accurate, and efficient solutions. This article explores innovative deep learning techniques, including convolutional neural networks (CNNs) and transformer-based models, that enhance disease detection in crops. Diseases of plants have a major impact on agricultural productivity and profitability, endangering our global food security. Minimizing output losses and managing vegetation properly depend on the quick and exact detection of diseased crops. Most disease indicators depend on inspecting by hand, which is frequently time-intensive, laborious, and false. By automate the illness identification process and enhancing accuracy through precise image analysis, machine learning (ML) techniques—in particular, deep learning (DL) approaches—offer an acceptable alternative. Using both deep learning and machine learning techniques, this paper offers a thorough analysis of recent advancements in the detection of plant diseases. We cover various approaches, datasets, preprocessing techniques, and constraints addressed. We investigate different datasets, preprocessing techniques, algorithms, and difficulties encountered in practical implementations. Trends such as multicrop disease detection systems, real-time monitoring, and explained artificial intelligence (XAI) are also covered in the text. The findings we report demonstrate the promise of utilizing machine learning (ML) techniques to agriculture, showing the urgent need of reliable datasets, strong models, and answers that are easy for farmer to execute.

Keywords: Multicrop identification, agriculture, explainable artificial intelligence (XAI), artificial intelligence, deep learning, image analysis, plant disease detection, and continuous monitoring.

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1. Ferentinos K P (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145: 311-318. doi.org/10.1016/j.compag.2018.01.009
2. Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D. Deep neural networks based recognition of plant diseases by leaf image classification. Computational intelligence and neuroscience. 2016;2016(1):3289801.
3. Mitra D, Gupta S, Srivastava D, Sani S. Plant disease identification using convolution neural networks. InIntelligent Internet of Things for Smart Healthcare Systems 2023 Feb 23 (pp. 203-215). CRC Press.
4. Pujari D, Yakkundimath R, Byadgi AS. SVM and ANN based classification of plant diseases using feature reduction technique. IJIMAI. 2016;3(7):6-14.
5. Weizheng S, Yachun W, Zhanliang C, Hongda W. Grading method of leaf spot disease based on image processing. In2008 international conference on computer science and software engineering 2008 Dec 12 (Vol. 6, pp. 491-494). IEEE.
6. Abbas A, Jain S, Gour M, Vankudothu S. Tomato plant disease detection using transfer learning with C-GAN synthetic images. Computers and electronics in agriculture. 2021 Aug 1;187:106279.
7. Too EC, Yujian L, Njuki S, Yingchun L. A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture. 2019 Jun 1;161:272-9.
8. Saleem MH, Potgieter J, Arif KM. Plant disease detection and classification by deep learning. Plants. 2019 Oct 31;8(11):468.
9. Amara J, Bouaziz B, Algergawy A. A deep learning-based approach for banana leaf diseases classification. InDatenbanksysteme für Business, Technologie und Web (BTW 2017)-Workshopband 2017 (pp. 79-88). Gesellschaft für Informatik eV.
10. Zhang S, Zhang S, Zhang C, Wang X, Shi Y. Cucumber leaf disease identification with global pooling dilated convolutional neural network. Computers and Electronics in Agriculture. 2019 Jul 1;162:422-30.
11. Weizheng S, Yachun W, Zhanliang C, Hongda W. Grading method of leaf spot disease based on image processing. In2008 international conference on computer science and software engineering 2008 Dec 12 (Vol. 6, pp. 491-494). IEEE.
12. Arnal Barbedo JG. Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus. 2013 Dec 7;2(1):660.
13. Hughes D, Salathé M. An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060. 2015 Nov 25.
14. Lu Y, Yi S, Zeng N, Liu Y, Zhang Y. Identification of rice diseases using deep convolutional neural networks. Neurocomputing. 2017 Dec 6;267:378-84.
15. Kamilaris A, Prenafeta-Boldu FX. Deep learning in agri-culture: a survey. computers and electronics in agriculture 147: 70–90 [Internet]. 2018
16. Sabzi S, Abbaspour-Gilandeh Y, García-Mateos G. A fast and accurate expert system for weed identification in potato crops using metaheuristic algorithms. Computers in Industry. 2018 Jun 1;98:80-9.
17. Sharma A, Aswal US, Rana A, Vani VD, Sankhyan A. Real Time Plant Disease Detection Model using Deep Learning. In2023 6th International Conference on Contemporary Computing and Informatics (IC3I) 2023 Sep 14 (Vol. 6, pp. 2695-2699). IEEE.
18. Wallelign S, Polceanu M, Buche C. Soybean plant disease identification using convolutional neural network. InFLAIRS 2018 May 10 (pp. 146-151).

Agriculture is the backbone of global food security, yet plant diseases significantly impact crop yields and farmers' livelihoods. Traditional disease detection methods are often time-consuming and require expert intervention, making them less feasible for large-scale farming. Recent advancements in deep learning have revolutionized plant disease identification by offering automated, accurate, and efficient solutions. This article explores innovative deep learning techniques, including convolutional neural networks (CNNs) and transformer-based models, that enhance disease detection in crops. Diseases of plants have a major impact on agricultural productivity and profitability, endangering our global food security. Minimizing output losses and managing vegetation properly depend on the quick and exact detection of diseased crops. Most disease indicators depend on inspecting by hand, which is frequently time-intensive, laborious, and false. By automate the illness identification process and enhancing accuracy through precise image analysis, machine learning (ML) techniques—in particular, deep learning (DL) approaches—offer an acceptable alternative. Using both deep learning and machine learning techniques, this paper offers a thorough analysis of recent advancements in the detection of plant diseases. We cover various approaches, datasets, preprocessing techniques, and constraints addressed. We investigate different datasets, preprocessing techniques, algorithms, and difficulties encountered in practical implementations. Trends such as multicrop disease detection systems, real-time monitoring, and explained artificial intelligence (XAI) are also covered in the text. The findings we report demonstrate the promise of utilizing machine learning (ML) techniques to agriculture, showing the urgent need of reliable datasets, strong models, and answers that are easy for farmer to execute.

Keywords: Multicrop identification, agriculture, explainable artificial intelligence (XAI), artificial intelligence, deep learning, image analysis, plant disease detection, and continuous monitoring.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Ferentinos K P (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145: 311-318. doi.org/10.1016/j.compag.2018.01.009
2. Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D. Deep neural networks based recognition of plant diseases by leaf image classification. Computational intelligence and neuroscience. 2016;2016(1):3289801.
3. Mitra D, Gupta S, Srivastava D, Sani S. Plant disease identification using convolution neural networks. InIntelligent Internet of Things for Smart Healthcare Systems 2023 Feb 23 (pp. 203-215). CRC Press.
4. Pujari D, Yakkundimath R, Byadgi AS. SVM and ANN based classification of plant diseases using feature reduction technique. IJIMAI. 2016;3(7):6-14.
5. Weizheng S, Yachun W, Zhanliang C, Hongda W. Grading method of leaf spot disease based on image processing. In2008 international conference on computer science and software engineering 2008 Dec 12 (Vol. 6, pp. 491-494). IEEE.
6. Abbas A, Jain S, Gour M, Vankudothu S. Tomato plant disease detection using transfer learning with C-GAN synthetic images. Computers and electronics in agriculture. 2021 Aug 1;187:106279.
7. Too EC, Yujian L, Njuki S, Yingchun L. A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture. 2019 Jun 1;161:272-9.
8. Saleem MH, Potgieter J, Arif KM. Plant disease detection and classification by deep learning. Plants. 2019 Oct 31;8(11):468.
9. Amara J, Bouaziz B, Algergawy A. A deep learning-based approach for banana leaf diseases classification. InDatenbanksysteme für Business, Technologie und Web (BTW 2017)-Workshopband 2017 (pp. 79-88). Gesellschaft für Informatik eV.
10. Zhang S, Zhang S, Zhang C, Wang X, Shi Y. Cucumber leaf disease identification with global pooling dilated convolutional neural network. Computers and Electronics in Agriculture. 2019 Jul 1;162:422-30.
11. Weizheng S, Yachun W, Zhanliang C, Hongda W. Grading method of leaf spot disease based on image processing. In2008 international conference on computer science and software engineering 2008 Dec 12 (Vol. 6, pp. 491-494). IEEE.
12. Arnal Barbedo JG. Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus. 2013 Dec 7;2(1):660.
13. Hughes D, Salathé M. An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060. 2015 Nov 25.
14. Lu Y, Yi S, Zeng N, Liu Y, Zhang Y. Identification of rice diseases using deep convolutional neural networks. Neurocomputing. 2017 Dec 6;267:378-84.
15. Kamilaris A, Prenafeta-Boldu FX. Deep learning in agri-culture: a survey. computers and electronics in agriculture 147: 70–90 [Internet]. 2018
16. Sabzi S, Abbaspour-Gilandeh Y, García-Mateos G. A fast and accurate expert system for weed identification in potato crops using metaheuristic algorithms. Computers in Industry. 2018 Jun 1;98:80-9.
17. Sharma A, Aswal US, Rana A, Vani VD, Sankhyan A. Real Time Plant Disease Detection Model using Deep Learning. In2023 6th International Conference on Contemporary Computing and Informatics (IC3I) 2023 Sep 14 (Vol. 6, pp. 2695-2699). IEEE.
18. Wallelign S, Polceanu M, Buche C. Soybean plant disease identification using convolutional neural network. InFLAIRS 2018 May 10 (pp. 146-151).