Grasshopper Optimization Algorithm and Neural Network Classifier for Detection and Classification of Barley Leaf Diseases

Abstract

The prevalence of plant diseases presents a substantial challenge to global agriculture, significantly impacting both production levels and economic stability in numerous countries. This study focuses on the early detection of two prevalent diseases affecting barley leaves: net blotch and spot blotch. We introduce a novel model designed for the accurate detection and classification of these diseases. The model employs advanced pre-processing techniques, including the transformation of images into the CIELAB color space and the segmentation of affected areas, to enhance disease identification accuracy. Key shape properties characterizing the diseased regions are extracted and analyzed to distinguish between the two diseases. A critical component of our approach is the feature selection phase, aimed at identifying the most pertinent and informative features, thereby minimizing classification errors and maximizing model accuracy with a minimal set of shape properties. To optimize this process, we have incorporated the Grasshopper Optimization Algorithm, which effectively identifies the optimal shape properties for feature selection. The final classification is executed using a Back Propagation Neural Network Classifier. The efficacy of our model was tested using images of barley afflicted with the specified diseases. The results were compelling, with the model achieving a remarkable accuracy rate, largely attributable to the integration of the grasshopper optimization algorithm in the feature selection stage.