Plant Leaf Disease Identification Using Deep Learning Algorithms

The use of machine learning and learning techniques to identify plant leaf categories and diagnose diseases has brought a wave of advancement in agriculture. This study focuses on the use of regression (LR) to identify and classify plant leaf diseases. Kaggle’s diverse datasets, covering a range of plant species and disease types, allow you to explore plant health. Feature extraction converts visual information about plant leaves into data, making it easier to train machine learning models. The LR model achieved an accuracy of 0.32, indicating that it is a promising tool for predicting leaf classes. Additionally, using Python and MATLAB, this study achieved an accuracy of 96.77% using the SVM model. These results highlight the impact of machine learning on disease detection in agriculture, which ultimately improves crop protection and food security measures. Standardized methods and detailed assessment methods form the basis of agricultural resource research and development. The combination of machine learning and artificial intelligence in leaf classification and disease diagnosis is leading to breakthroughs in agriculture.