Rice Leaf Disease Detection Using Image Processing Techniques

One of the factors that have contributed significantly to human civilization's development is agriculture. Automation has started to control every industry in this modern era. However, compared to other industries, farm automation is not as sophisticated. As a result, the need for an automation system in agriculture is pressing. Finding illnesses in crops at an early stage of growth is one of the main problems that farmers confront. This research aims to develop an autonomous drone to identify diseases at the earliest and prevent the crop from diseases. Plant or crop diseases play a significant role in both value and amount in agriculture, it is significantly effective to identify and diagnose these disorders. A significant scientific difficulty is a proper classification with small datasets in machine learning. Disease categorization, image processing, pattern recognition, and disease detection are all accomplished using the neural network whale optimization method. The neural network whale optimization is suggested in this research study as a way to identify rice leaf diseases. This study presented 5932 on-field photos of four different rice leaf diseases, including bacterial blight, blast, brown spot, and tungro.