Abstract

The productivity in agriculture is a major factor in the economy. As a result, disease detection in plants plays a significant role in agriculture. If sufficient care is not taken in this regard, then it can have major impacts on plants by affecting the quality, quantity, or productivity of the respective product or service. In addition to reducing the amount of labor required to monitor huge farms of crops, automatic disease detection detects symptoms at an early stage, i.e., when they first develop on plant leaves. A method for picture segmentation is presented in this study, which is utilized for the automatic categorization of banana leaf diseases. The images are used to detect and classify diseases in banana plants. This is a cost-effective and efficient way for farmers to monitor the plant’s health. The images must be segmented in order to evaluate and extract information from them. This module of image processing isolates the object of interest from the rest of the image, allowing for more detailed analysis. As a result, the success of a higher-level image processing modules is determined by the precision with image segmentation modules being carried out. For segmentation and classification, a hybrid fuzzy C-means procedure is used. Additionally, in order to identify banana plant illnesses, the color, shape, and texture characteristics were extracted. To compare the suggested method with the existing deep learning methods, for diseases such as black sigatoka, yellow sigatoka, dried/old leaves, banana bacterial wilt with healthy plants, several quantitative metrics were investigated.

Key words: Agriculture; Banana Leaf; Classification; Disease Detection; Deep Learning; Segmentation;