Precision Agriculture Deep Neural Network Driven Multi-hop Plant Image Noisy Data Transmission and Plant Disease Detection

• Main Authors: Asiedu, Derek Kwaku Pobi, Ofori-Amanfo, Kwadwo Boateng, Bennin, Kwabena Ebo, Benjillali, Mustapha, Lee, Kyoung-Jae, Gookyi, Dennis Agyemanh Nana, Saoudi, Samir
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial neural networks; autoencoder; convolutional neural network (CNN); Deep learning; Deep learning (DL); multi-hop wireless sensor network (WSN); plant disease detection (PDD); Plant diseases; Prediction algorithms; Routing; Spread spectrum communication; Wireless sensor networks
• ISSN: 2832-8337
• DOI: 10.1109/ISIVC61350.2024.10577918

Under precision agriculture (PA), plant disease detection (PDD) is imperative regarding farm crops' life quality and crop yield. However, the data captures for PDD is influenced by the noisy data captured by farm sensors due to wireless noisy transmission channels. Hence, this work considers the onsite or offsite (remote) farm PDD through onsite farm monitoring PA sensor networks (PAN). Here, effects on captured sensor image (plant leaf image) data transmitted through the PAN to an PDD application are studied. Where both traditional decodeand-forward (DF) data routing and channel-effect considering machine learning data autoencoder routing are used for image data transmission. In addition, a PDD deep learning algorithm is developed to predict whether or not a farm plant is diseased, based on the noisy image data captured by the PAN through data routing. From the PAN-PDD simulation, the proposed ML PANPDD algorithm showed fair performance over the DF PAN-PDD.