Optimal Fuzzy Deep Neural Networks-Based Plant Disease Detection and Classification on UAV-Based Remote Sensed Data

Unmanned Aerial Vehicles (UAVs) equipped with remote sensing (RS) abilities have revolutionized agriculture practices, especially in the area of plant disease detection, with the incorporation of deep learning (DL) techniques. Using the UAVs to capture high-resolution images of agricultural regions, RS information becomes widely accessible for monitoring and identifying crop health and signs of diseases. DL models, specifically convolutional neural networks (CNNs), are employed to analyze these images, enabling automated recognition and detection of plant diseases with enhanced accuracy. This integration of UAV-based RS and DL models facilitates early disease detection, allowing farmers to implement timely interventions, such as targeted pesticide application or crop management strategies, ensuring food security and reducing yield losses. Moreover, this model’s scalability and efficiency improve its value for precision agriculture, optimizing resource usage and promoting sustainable farming practices. This manuscript proposes an Optimal Fuzzy Deep Neural Networks-based Plant Disease Detection and Classification (OFDNN-PDDC) technique on UAV-based Remote Sensed Data. The purpose of the OFDNN-PDDC technique is to proficiently detect and classify the distinct kinds of plant diseases. The OFDNN-PDDC technique follows a three-stage process to enhance plant disease detection performance. At the primary level, the OFDNN-PDDC technique employs an improved ShuffleNetv2 model for learning complex and intrinsic feature patterns on the RS data. Besides, the OFDNN-PDDC technique utilizes a fuzzy restricted Boltzmann machine (FRBM) model to detect plant diseases. Finally, the hyperparameter selection of the OFDNN-PDDC technique is performed by the tent chaotic salp swarm algorithm (TCSSA) model. To illustrate the superior outcomes of the OFDNN-PDDC technique, detailed experiments were accomplished by utilizing APD and CPD datasets. The experimental validation of the OFDNN-PDDC technique portrayed a superior accuracy value of 96.18% and 98.85% over existing techniques under the used datasets.