Automatic early detection of rice leaf diseases using hybrid deep learning and machine learning methods

Plant leaf disease detection is critical for long-term agricultural viability. Numerous Artificial Intelligence (AI) and Machine Learning (ML) technologies have been implemented for detecting rice diseases. However, such methods failed to identify or have slow recognition causing severe output loss. Therefore, an advanced and precise detection method has become necessary to overcome this issue. This study analyzes plant diseases that affect rice, comprising three different forms of diseases. Bacterial leaf blight, Brown spot, and Leaf smut are three of the six diseases that can affect rice plants. In the proposed approach a VGG-16 transfer learning with Faster R-CNN deep architecture is used to extract features. After completing the transfer learning step, the gathered characteristics are categorized using the random forest method. The random forest classifier divided the radish field into three distinct regions. The images of rice plant leaves are taken from UCI Machine Learning Repository. The proposed approach obtains an average predicting accuracy of 97.3% for rice disease imagery class prediction. The extensive experiment outcomes demonstrate the suggested technique's validity, so it effectively detects rice diseases.