An Adapted Moth Search with Convolutional Neural Network with Replicator Neuron-Based Leaf Disease Detection
Keywords:
plant disease, Internet of Things, artificial intelligence, Convolutional Neural Network, Spiking Neuron, Adapted Moth SearchAbstract
Crop quality and yield can be significantly impacted by plant diseases, and even though plants may be examined for indicators of illness by trained biologists or farmers, this is typically an inexact and labor-intensive process. This study employs IoT and AI-based monitoring strategies to design and develop a smart method for classifying leaf illnesses. So as to measure the effectiveness of these two approaches, simulation results are compared in this work. In the first section, the data of photos of plants from the Plant Village data set augmented using a Hybrid CNN (Convolutional Neural Network) with RNN (Replicator Neural Network) and named as HCRNN, and deep features mined from these images. So as to enhance the precision of the segmentation procedure, the plant images undergo preliminary processing with an adaptive kaun filter. Next, a Glowworm Swarm Optimization based Clustering (GSOC) technique is used to isolate the plant region in the processed image. The HCRNN was then used to classify the plant disease based on the retrieved features. The projected method uses the Adapted Moth Search (AMS) Algorithm to fine-tune the CNN's (Convolutional Neural Network) hyperparameter in order to enhance its classification accuracy. Two branches of the model are used to learn from the T2- and Diffusion-weighted MRI data: one employs a ten-layer CNN After applying HCRNN to classify the relevant characteristics, and then assesses the quality of the classification in terms of precision, recall, and f-score. Extensive field testing indicates that the technique is useful in hot and humid environments and that it is more accurate than other categorization schemes at recognizing classes of disease in leaves.
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