Chili Leaf Disease Detection Using Deep Feature Extraction

Abstract

Please Diseases in crops lead to decreased production, which can be addressed through consistent surveillance. Manual surveillance of crop diseases is both arduous and prone to mistakes. The timely identification of crop leaf diseases using Computer Vision and Artificial Intelligence can aid in minimizing the negative impact of diseases and address the limitations of continuous human surveillance. To classify chili crop diseases, this research paper introduces a new deep feature extraction model based on Transfer Learning using ResNet50, MobileNet, EfficientNetB0, and multiple classifiers. On Plant Village dataset related to the diseases of the chili crop and Private data set, the proposed method was trained and tested. And also analyzed the results by comparing the performance of the pre-trained deep learning models on original data and data filtered through the Image filtering mechanisms and proposed method on the plant village dataset and private dataset, the highest performance accuracy is 99.6% with ResNet50 and the faster CPU time for feature extraction is 29.3 seconds using MobileNet. Comparing the suggested model to the most advanced deep learning models reveals greater accuracy with fewer computational resources.