Plant leaf disease detection using hybrid grasshopper optimization with modified artificial bee colony algorithm

The importance of plants is acknowledged because they provide the majority of human energy. due to their medicinal, nutritional, & other benefits. Any time during growing crops, plant diseases can affect the leaf, which can cause significant crop production losses and market value reduction. In this paper, three optimization techniques are utilized to detect plant leaf disease. The input image has some noise signal which is removed by using the Modified Wiener Filter (MWF), this is the pre-processing stage of the proposed methodology. Feature Extraction is performed using Improved Ant Colony Optimization (IACO), this will extract the important features. The proposed model is described as Hybrid Grasshopper Optimization with a modified Artificial Bee Colony Algorithm (HyGmABC), which is used for classification. This will check whether the disease is present in the leaf region or not. The performance of the proposed methodology is evaluated using the performance metrics like accuracy, precision, recall, False Negative Ratio (FNR), Negative Prediction Value (NPV), and Matthews correlation coefficient (MCC). The plant village dataset is chosen for implementation. The proposed methodology produces high accuracy of 98.53% which is higher than the existing techniques.