Performance Analysis of Pre-Processing techniques in Plantain Tree Disease Classification with Convolutional Neural Network

The agricultural sector is considered as the backbone of almost all economies present in the world. Crop protection is remaining as a crucial factor in agriculture, since it determines the quantity and quality of products and increases the profit of the farmers. In the Indian economy, plantain tree cultivation is playing a major role. Plantain Tree is highly exposed to diseases such as Black Sigatoka/Yellow Sigatoka, Panama, Bunchy top, Moko, chlorosis, etc. This study aims at developing a DL model to facilitate crop protection by accurately identifying the diseases that affect the productivity of plantain trees by using the leaf images. The pre-processing of image data will also help to improve the classification accuracy and training time. The main aim of this study is the research investigation and performance comparison of various data augmentation and feature selection techniques that helps in the classification and detection of diseases in plantain trees. The experiments are carried out in real-time datasets, which are collected from different plantain tree cultivation in different fields of Tamil Nadu. The experimental results reveal the necessity to propose effective preprocessing techniques for improving the accuracy of the classification models.