High Accurate Unhealthy Leaf Detection

TL;DR

This paper introduces a comprehensive model for early detection of plant leaf diseases using advanced image processing, segmentation, and classification techniques to improve accuracy and aid in crop protection.

Contribution

The paper presents an integrated approach combining CNN, adversarial networks, genetic algorithms, GLCM, and SVM for high-accuracy plant disease detection.

Findings

High accuracy in disease classification achieved

Effective segmentation of leaf images using genetic algorithms

Enhanced image preprocessing improves detection performance

Abstract

India is an agriculture-dependent country. As we all know that farming is the backbone of our country it is our responsibility to preserve the crops. However, we cannot stop the destruction of crops by natural calamities at least we have to try to protect our crops from diseases. To, detect a plant disease we need a fast automatic way. So, this paper presents a model to identify the particular disease of plant leaves at early stages so that we can prevent or take a remedy to stop spreading of the disease. This proposed model is made into five sessions. Image preprocessing includes the enhancement of the low light image done using inception modules in CNN. Low-resolution image enhancement is done using an Adversarial Neural Network. This also includes Conversion of RGB Image to YCrCb color space. Next, this paper presents a methodology for image segmentation which is an important aspect for identifying the disease symptoms. This segmentation is done using the genetic algorithm. Due to this process the segmentation of the leaf Image this helps in detection of the leaf mage automatically and classifying. Texture extraction is done using the statistical model called GLCM and finally, the classification of the diseases is done using the SVM using Different Kernels with the high accuracy.