GNN Based Cauliflower Plant Disease Prediction Using Deep Learning Techniques
Keywords:
Graph Neural Network, Plant Disease Prediction, Deep Learning, Internet of ThingsAbstract
A vital component of agriculture, vegetables are essential for maintaining people's overall health. The information systems can assist vegetable producers in producing high yields that support sustainable farming practices and help ensure global food security. The common vegetable cauliflower (Brassica oleracea var. botrytis) is susceptible to several illnesses that can reduce output and performance. Furthermore, deep learning-based disease identification systems that can assist farmers in identifying cauliflower infections and enabling them to respond promptly have yet to be created for the crop. This study suggests an automated machine vision-based expertise method for identifying cauliflower illnesses. A Graphical Neural Network based Plant Disease Prediction (GNN-PDP) model is designed in this research using the Internet of Things (IoT) and deep learning algorithms. A cell phone or other IoT portable device's taken picture is analysed and then classed to detect disease to help cauliflower growers. Depending on feature extraction, the algorithm categorizes four illnesses in cauliflowers, including bacterial softness, white rusting, black rotting, and downy mildew. This study makes use of 750 photos in total. Before extracting two attributes, including statistical and co-occurrence variables, the Cat Swarm Optimization (CSO) technique was performed on the collected pictures to segment the disease-affected areas. Six classification algorithms— CNN, DNN, RF, DT, LDA, and PCA —were utilized for illness classification comparison. Their performance was assessed using different efficiency measures. With an efficiency close to 89%, it was discovered that the GNN classification performed better than all other classifications for identifying cauliflower illness.
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