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New Innovation and Idea | Information Technology | India | Volume 12 Issue 5, May 2023 | Rating: 5.4 / 10
Tomato Plant Diseases Detection and Classification
Sameh Mohammed Abdo Khaled
Abstract: This project aims to develop a machine learning model to detect and classify diseases in tomato plants. The increasing demand for food and the need to maintain food security has led to the development of advanced technologies in the agriculture sector. One of the most challenging tasks in agriculture is to identify and diagnose diseases in crops, which can significantly reduce their productivity. In this project, we will use computer vision and machine learning techniques to automate the process of disease detection and classification in tomato plants. The first step in this project will be to collect a large dataset of tomato plant images and annotate them with disease labels. This dataset will be used to train a convolutional neural network (CNN) model. The CNN model will learn to recognize patterns in the images that are associated with specific diseases. We will then use this trained model to classify new images of tomato plants and detect the presence of any diseases. The performance of the model will be evaluated based on accuracy, precision, recall, and F1 score. The results of this project will be compared to existing methods in the literature to demonstrate the effectiveness of the proposed approach. In conclusion, this project has the potential to make a significant contribution to the field of plant disease detection and classification. By automating the process of disease detection, we can help farmers to quickly and accurately identify diseases in their crops, reducing the impact of diseases on productivity and improving food security. The results of this project will also provide a foundation for further research in the area of computer vision and machine learning for agriculture.
Keywords: Convolutional Neural Network, bacterial infections, tomato disease identification, Late blight, Spider mites, Bacterial Spot on tomato leaves
Edition: Volume 12 Issue 5, May 2023,
Pages: 1078 - 1084