Prediction of Crop Leaf Health by MCCM and Histogram Learning Model Using Leaf Region

Abstract

This study introduces a model called the crop leaf health prediction model (CLHPM) that utilizes a bio-inspired method to accurately identify the leaf region. This approach enhances the process of learning important features and overcomes the challenges posed by the hindrance from the chromatic and structural diversity of each leaf. To train the learning model, a modified co-occurrence matrix (MCCM) in texture analysis is used to overcome the limitations of the leaf region, and a histogram method is also deployed for color analysis. The experiment is conducted on a real dataset of tomato crop leaves. It is observed that the average accuracy has increased by 3.50%. The existing MobileNetV2 model presents an accuracy of 95.73%, and the proposed CLHPM model renders 99.23%. Moreover, an enhancement of 3.72 in the F-measure is also noticed.