Agriculture is the primary source of the economy and food in various regions. The influence of the plant productivity has a high impact on the nation’s economy. The weather conditions and the climate changes result in the food crops agonizing from the latest disorders and creating them highly in danger to the pests. The identification of the plant disorder is significant as it has a high influence on the profits and productivity of the farmers. Early identification of the plant disorder can support the farmers in creating the essential activities to secure the productivity. Conventional mechanisms of plant disorder identification utilize the knowledge of the human for evaluating the plant disorder. But, the manual detection of the plant disorder is time-consuming and tiresome operation and needs the knowledge of the experts for better detection; hence the necessary remedy can be performed. Therefore, the machine learning-aided plant disorder approach requires to be enhanced for the correct estimation of the plant disorder. In this task, an efficient plant disease identification system is improved by applying a machine learning model. The images of the plants are collected from the benchmark data assets. The collected image is given to the watershed algorithm. Watershed is an advanced image segmentation technique utilized to segment the abnormality image. From the segmented image, the color and texture features are extracted. The segmented image from the watershed algorithm is now provided as input to the Adaptive Weighted Neural Network (AWNN). The parameters and the weights in the NN are optimized with the aid of a Modified Random Number of Cheetah Optimizer (MRNCO). The final detected plant disease is obtained from the AWNN architecture.
Plant Disease Detection; Watershed Algorithm; Texture And Color Features; Modified Random Number Of Cheetah Optimizer; Adaptive Weighted Neural Network
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