Optimized classification model for plant diseases using generative adversarial networks

The agricultural industry, the service sector, and the food processing industry are just a few of the many aspects that affect a country's economy. One of the most important sectors of the economy in our nation is agriculture. The agriculture industry, however, encounters numerous challenges, such as diverse climatic conditions in various parts of our nation that give rise to various infectious illnesses in various plant sections, leading to a significant decline in crop output and income generation. An essential improvement in the framework for harvest formation is the early and accurate detection of plant diseases. Because it demands knowledge, the traditional approach of eye observation is ineffective for detecting plant diseases. Machine learning (ML) approaches are being used to start the identification and classification of plant illnesses to solve this problem. This project offers an analysis of these various methods. A review of different ML techniques for accurate plant disease identification is done. The major areas of system design, model design, value prediction from observation, and experience from the massive amount of data and diverse gathering are the focus of machine learning (ML), a subset of artificial intelligence techniques. Optimized convolutional neural networks are used in this study to classify various plant leaf diseases. The dataset is enhanced using a generative adversarial network. The model is trained and tested using data from PlantVillage. Images of plant diseases on pepper, tomato, and potato plants are included in the dataset. The classifier is trained and tested using 15 categories of plant diseases. The model's overall accuracy is 98%.