Abstract
For the past two decades, the imbalance between food supply and population growth has been a major concern. Agriculture plays an important role in human development, and technological improvements have significantly contributed to this process. In this study, Convolutional Neural Networks (CNNs) will be utilized to identify plant leaf diseases based on leaf images. The objective is to develop an application that accurately classifies plant images as healthy or diseased. This will be achieved by collecting and preprocessing a dataset of damaged and healthy plant images under varying watering conditions. Globally, essential agricultural commodities such as tomatoes, cotton, paddy, etc. often experience price fluctuations due to supply and demand issues. Additionally, many farmers lack access to agricultural specialists for diagnosing and treating leaf diseases. To solve this issue, a low-cost image processing technique is developed to detect leaf diseases in tomato plants, an essential ingredient in Indian kitchens. Using the CNN models, farmers can compare images of diseased leaves and detect infections early, enabling timely intervention. This approach benefits both farmers and consumers by stabilizing the prices of, tomatoes as it is rapid, cost-effective, and applicable year-round.
References
- Panigrahi, Kshyanaprava Panda, Himansu Das, Abhaya Kumar Sahoo, and Suresh Chandra Moharana. "Maize leaf disease detection and classification using machine learning algorithms." In Progress in Computing, Analytics and Networking: Proceedings of ICCAN 2019, Springer Singapore, 2020. 659-669.
- Moyazzoma, Raida, Md Al Amin Hossain, Md Hasanuzzaman Anuz, and Abdus Sattar. "Transfer learning approach for plant leaf disease detection using CNN with pre-trained feature extraction method Mobilnetv2." In 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), IEEE, 2021. 526-529.
- Rakhra, Manik, Arun Singh, Gaurav Chawla, Jatin Rana, Aman Kumar, P. Teja, and Vikrant Sharma. "Plant Leaf Disease Detection Using Machine Learning." In 2023 1st International Conference on Circuits, Power and Intelligent Systems (CCPIS), IEEE, 2023. 1-6.
- Albattah, Waleed, Marriam Nawaz, Ali Javed, Momina Masood, and Saleh Albahli. "A novel deep learning method for detection and classification of plant diseases." Complex & Intelligent Systems (2022): 1-18.
- Saleem, Muhammad Hammad, Johan Potgieter, and Khalid Mahmood Arif. "Plant disease detection and classification by deep learning." Plants 8, no. 11 (2019): 468.
- Jasim, Marwan Adnan, and Jamal Mustafa Al-Tuwaijari. "Plant leaf diseases detection and classification using image processing and deep learning techniques." In 2020 International Conference on Computer Science and Software Engineering (CSASE), IEEE, 2020. 259-265.
- Ale, Laha, Alaa Sheta, Longzhuang Li, Ye Wang, and Ning Zhang. "Deep learning based plant disease detection for smart agriculture." In 2019 IEEE Globecom Workshops (GC Wkshps), IEEE, 2019. 1-6.
- Applalanaidu, Majji V., and G. Kumaravelan. "A review of machine learning approaches in plant leaf disease detection and classification." In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), IEEE, 2021. 716-724.
- Jackulin, C., and S. Murugavalli. "A comprehensive review on detection of plant disease using machine learning and deep learning approaches." Measurement: Sensors 24 (2022): 100441.
- Ferentinos, Konstantinos P. "Deep learning models for plant disease detection and diagnosis." Computers and electronics in agriculture 145 (2018): 311-318.