Study on Tomato Disease Classification based on Leaf Image Recognition based on Deep Learning Technology

Abstract

The utilization of computer vision technology is of the utmost significance in the examination of plant diseases. Research utilizing image processing to investigate plant diseases necessitates the analysis of discernible patterns on plants. Recently, numerous image processing and pattern classification techniques have been employed in the construction of a digital vision system capable of recognizing and categorizing the visual manifestations of plant diseases. Given the abundance of algorithms formulated for the purpose of plant leaf image classification for the detection of plant diseases, it is imperative to assess the accuracy of each algorithm, as well as its potential to identify diverse disease types. The main objective of this study is to explore accurate deep learning architectures that are more effective in deploying and detecting tomato diseases, thus eliminating human error when identifying tomato diseases through visual observation. and get more widespread use. An initial model was constructed from the ground up using a convolutional neural network (CNN), which was trained with 22930 tomato leaf images, and then compared to VGG16, Mobile Net, and Inceptionv3 architectures through a fine-tuning process. The basic CNN model achieved a training accuracy of 90%, whereas the training accuracies of VGG16, Mobile Net, and Inceptionv3 were respectively observed to be 89%, 91%, and 87%. The VGG16 model has a greater computational complexity than other approaches due to its considerable quantity of predefined parameters. Despite to be simpler, MobileNet proved to be the most efficient in terms of accuracy and thus is the most suitable for this research, due to its lightweight structure, fast functioning and adaptability for mobile devices. In contrast to other architectures, the suggested CNN architecture exhibits shallower characteristics, facilitating faster training on the same dataset. This research will provide a solid foundation for future scholars to easily improve the categorization of plant diseases, which is to develop algorithms that are lighter, faster, easier to run, and have higher accuracy.