Deepharvest: Revolutionizing Agriculture Through a Variofusionnet and Featexpronet for Accurate and Timely Leaf Disease Detection and Management
Keywords:
Alex Net, DenseNet-121, Efficient Net, Generative Adversarial Networks, Google Net, Modified Gaussian Smoothing technique, Region Proposal Networks, ResNet-50, Vision Transformer, YOLO and FeatExProNet.Abstract
In this pioneering approach to rust classification in plant leaves, deploy an exhaustive pre-processing pipeline to fortify the robustness of this dataset. The integration of Generative Adversarial Networks serves to augment the dataset, while a groundbreaking Modified Gaussian Smoothing technique is introduced to effectively mitigate noise and elevate image quality. Feature extraction is bolstered through Contrast Stretching, enhancing contrast, and color correction methods adeptly standardize color variations. Precision in disease-affected area identification is achieved through refined leaf localization using Region Proposal Networks (RPN) and this innovative Spatial Attention Mechanisms. Further optimization in Regions of Interest (ROI) identification is realized with an optimized dual attention YOLO and FeatExProNet combination, extracting key features encompassing shape, color, texture, statistics, and deep learning-based attributes. Feature selection employs a Hybrid Optimization Approach, synergizing Binary Sand Cat Swarm Optimization and Butterfly Optimization algorithms. The conclusive step incorporates a VarioFusionNet-based model, seamlessly amalgamating Vision Transformer, Google Net, Alex Net, DenseNet-121, ResNet-50, and Efficient Net to ensure unparalleled accuracy in leaf disease detection. This comprehensive methodology represents a remarkable leap forward in rust classification, offering a commitment to improved accuracy and robustness in the identification of plant leaf diseases.
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