Detection Of Leaf Diseases in Pulses, Fruits and Vegetables

Abstract

Agriculture is critical to our economic well-being. A wide range of illnesses may have a significant impact on the yield. Detection of plant diseases at an early stage is critical to avoiding significant harm. Farmers will benefit more from automated systems that check aberrant signs in the development of plants. An automated approach for the prediction and categorization of plant leaf diseases is investigated in this research. Additionally, a look at several methods for diagnosing plant leaf diseases is covered in-depth. The suggested system would use image processing and segmentation techniques methods to detect leaf diseases in pulses, fruits and vegetable plants. An enhanced CNN model was constructed and used. An enhanced CNN model is constructed and trained on a dataset of 20,600 photo. In order to increase system prediction accuracy and the categorization of genuine positive samples, optimization is carried out. This model induced 93.18 percent more accurate predictions may be made for 3 different species with twelve distinct illnesses using the approach now under consideration.