Integrated Feature Fusion in Multiclass Maize Leaf Disease Recognition

Abstract

 Plant diseases are the main factor in plant mortality and destruction, especially in trees. Early discovery, however, can assist to manage and treat this issue efficiently. To increase output, crop and plant lesions are detected and stopped as soon as feasible. Because it relies solely on visual observation, manual inspection of plant leaf diseases is time-consuming and expensive. The authors offer methods for identifying and categorizing plant leaf diseases using computer vision. Pre-processing original images to visualize contaminated areas, feature extraction from unprocessed or segmented images, feature fusion, feature selection, and classification are a few examples of computer vision approaches. The fusion technique is used to combine the target's numerical data features, which go beyond the picture, with the extracted image features to increase the target's feature representation. The following are the principal issues that researchers found in the literature: Low-contrast infected regions. Extract redundant and irrelevant information, which degrades classification accuracy; Redundant and irrelevant information may lengthen computation times and the targeted models performance will suffer as a result. This study proposed a framework for classifying plant leaf diseases based on the best feature selection and a deep learning fusion model. In the suggested approach, contrast is first enhanced using a pre-processing model, and then the issue of an unbalanced dataset is resolved via data augmentation. The proposed Deep Fusion Learning Model (DFLM) shows an accuracy of 98.8% in comparison with other models.