Fuzznet: Revolutionizing Plant Disease Detection with Deep Learning and Fuzzy Logic Integration

Abstract

Integrating deep learning techniques with the noise-handling capabilities of fuzzy logic presents a promising approach for enhancing the efficacy of plant disease detection systems. This paper proposes a methodology leveraging Deep Neural Networks (DNN) and Fuzzy Logic to address the challenges of detecting and recognising plant diseases. The methodology combines the powerful learning abilities of deep neural networks with the interpretability and noise resilience of fuzzy logic and incorporates fuzzy logic rules into the deep learning framework. The proposed system, a Fuzzy-based deep learning network (FuzzNet), aims to improve the robustness and generalizability of plant disease detection models across diverse plant species and illnesses. The methodology includes the design of the FuzzNet architecture, the steps taken for data preprocessing, the procedures used for model training, and the metrics used for evaluation. The experiments rely on the PlantVillage dataset, a comprehensive labelled image collection showcasing different plant diseases. The role of remotely sensed data in this methodology is crucial, as it enhances the performance of FuzzNet models by providing valuable insights into plant health status. This paper discusses the methods of integrating deep learning and fuzzy logic, explores the role of remotely sensed data in enhancing detection accuracy, and presents promising findings in reliably identifying and classifying plant diseases.

Keywords: Deep learning, Fuzzy logic, Plant Disease Detection, Noise Handling, Fuzzy Deep Neural Networks (FDNN), Remotely Sensed Data (RSD)