Every day, countless grains and fruits are cultivated around the world. To keep up with the growing population’s needs, farmers are constantly adopting new and advanced techniques. Furthermore, the development and application of new technologies enable the quick and simple detection of plant diseases. Identifying plant diseases at an early stage is crucial to minimizing their effects on crop productivity and quality. Traditional diagnostic methods often rely on expert knowledge and can be slow. However, with AI, this process can be automated, allowing farmers to quickly identify problems and take prompt action to address them. Using computer vision algorithms, we examined high-resolution images of peaches and corn leaves to spot signs of diseases like peach scab, bacterial spot, and fungal infections in corn, such as gray leaf spot and northern corn leaf blight. In this study, we used artificial intelligence and computer science to identify the ailments and offer treatments using pictures of a fruit called a peach and a diseased leaf of a grain called corn.

Keywords:Maize leaf, SVM, KNN, ML, plant disease detection

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1. Tran HAM, Ngo HQT, Nguyen TP, Nguyen H. Design of green agriculture system using Internet of Things and image processing techniques. In: 4th International Conference on Green Technology and Sustainable Development (GTSD); 2018; IEEE.
2. Wang J, Lu B, Yu K, Tian M, Huang X, Wang Z. Rice growth monitoring using multi-temporal GF-1 images. In: 2017 6th International Conference on Agro-Geoinformatics; 2017. DOI: 10.1109/agrogeoinformatics.2017.8047018.
3. Rathod AN, Tanawal B, Shah V. Image processing techniques for detection of leaf disease. Int J Adv Res Comput Sci Software Eng. 2013;3(11):397–9.
4. Zhang S, Wang H, Huang W, You Z. Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG. Optik. 2018;157:866–72.
5. Olawepo S, Adebiyi A, Adebiyi M, Okesola O. An overview of smart garden automation. In: International Conference on Mathematics, Computing Engineering and Computer Science; 2020; IEEE.
6. Amin SRM, Awang R. Automated detection of nitrogen status on plants: performance of image processing techniques. In: 2018 IEEE Conference; 2018.
7. Arivazhagan S, Shebiah RN, Ananthi S, Vishnu Varthini S. Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int: CIGR J. 2013;15(1):1–6.
8. Gaikwad VP, Musande V. Wheat disease detection using image processing. Aurangabad, India; 2017. pp. 110–2.
9. Panchal P, Raman VC, Mantri S. Plant diseases detection and classification using machine learning models. In: 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS); 2019. pp. 1–6; IEEE.
10. Jiang Z, Dong Z, Jiang W, Yang Y. Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning. Comput Electron Agric. 2021;186:106184.
11. Nandhini SA, Hemalatha R, Radha S, Indumathi K. Web-enabled plant disease detection system for agricultural applications using WMSN. Wirel Personal Commun. 2018;102(2):725–40.
12. Zeng et al. Improvement of SKPSNet-50 models for classifying eight kinds of maize leaf diseases. 2022.
13. Agarwal et al. Development of an efficient CNN model for tomato disease identification achieving 93.5% accuracy. 2020.
14. Azgomi et al. Design of a multi-layer neural network for classifying four diseases with low computation cost. 2023.
15. Kumar et al. Proposal of an automatic system for tomato leaf disease recognition based on deep convolutional neural network (DCNN). 2022.
16. Ma et al. Proposal of an improved YOLOv5n model to identify common maize leaf spots, gray spots, and rust diseases for mobile applications. 2023.
17. Sun et al. Proposal of an improved InceptionV3 model based on deep data yielding a maturity class classification accuracy of up to 98%. 2021.
18. Fei et al. Proposal of an improved ShuffleNet V2 for fresh cut flowers classification achieving 99.915% accuracy in the RGB-D flowers dataset. 2023.

Every day, countless grains and fruits are cultivated around the world. To keep up with the growing population’s needs, farmers are constantly adopting new and advanced techniques. Furthermore, the development and application of new technologies enable the quick and simple detection of plant diseases. Identifying plant diseases at an early stage is crucial to minimizing their effects on crop productivity and quality. Traditional diagnostic methods often rely on expert knowledge and can be slow. However, with AI, this process can be automated, allowing farmers to quickly identify problems and take prompt action to address them. Using computer vision algorithms, we examined high-resolution images of peaches and corn leaves to spot signs of diseases like peach scab, bacterial spot, and fungal infections in corn, such as gray leaf spot and northern corn leaf blight. In this study, we used artificial intelligence and computer science to identify the ailments and offer treatments using pictures of a fruit called a peach and a diseased leaf of a grain called corn.

Keywords:Maize leaf, SVM, KNN, ML, plant disease detection

🔒 This is a subscription article

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

1. Tran HAM, Ngo HQT, Nguyen TP, Nguyen H. Design of green agriculture system using Internet of Things and image processing techniques. In: 4th International Conference on Green Technology and Sustainable Development (GTSD); 2018; IEEE.
2. Wang J, Lu B, Yu K, Tian M, Huang X, Wang Z. Rice growth monitoring using multi-temporal GF-1 images. In: 2017 6th International Conference on Agro-Geoinformatics; 2017. DOI: 10.1109/agrogeoinformatics.2017.8047018.
3. Rathod AN, Tanawal B, Shah V. Image processing techniques for detection of leaf disease. Int J Adv Res Comput Sci Software Eng. 2013;3(11):397–9.
4. Zhang S, Wang H, Huang W, You Z. Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG. Optik. 2018;157:866–72.
5. Olawepo S, Adebiyi A, Adebiyi M, Okesola O. An overview of smart garden automation. In: International Conference on Mathematics, Computing Engineering and Computer Science; 2020; IEEE.
6. Amin SRM, Awang R. Automated detection of nitrogen status on plants: performance of image processing techniques. In: 2018 IEEE Conference; 2018.
7. Arivazhagan S, Shebiah RN, Ananthi S, Vishnu Varthini S. Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int: CIGR J. 2013;15(1):1–6.
8. Gaikwad VP, Musande V. Wheat disease detection using image processing. Aurangabad, India; 2017. pp. 110–2.
9. Panchal P, Raman VC, Mantri S. Plant diseases detection and classification using machine learning models. In: 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS); 2019. pp. 1–6; IEEE.
10. Jiang Z, Dong Z, Jiang W, Yang Y. Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning. Comput Electron Agric. 2021;186:106184.
11. Nandhini SA, Hemalatha R, Radha S, Indumathi K. Web-enabled plant disease detection system for agricultural applications using WMSN. Wirel Personal Commun. 2018;102(2):725–40.
12. Zeng et al. Improvement of SKPSNet-50 models for classifying eight kinds of maize leaf diseases. 2022.
13. Agarwal et al. Development of an efficient CNN model for tomato disease identification achieving 93.5% accuracy. 2020.
14. Azgomi et al. Design of a multi-layer neural network for classifying four diseases with low computation cost. 2023.
15. Kumar et al. Proposal of an automatic system for tomato leaf disease recognition based on deep convolutional neural network (DCNN). 2022.
16. Ma et al. Proposal of an improved YOLOv5n model to identify common maize leaf spots, gray spots, and rust diseases for mobile applications. 2023.
17. Sun et al. Proposal of an improved InceptionV3 model based on deep data yielding a maturity class classification accuracy of up to 98%. 2021.
18. Fei et al. Proposal of an improved ShuffleNet V2 for fresh cut flowers classification achieving 99.915% accuracy in the RGB-D flowers dataset. 2023.