A Review of Soybean Crop Classification and Disease Identification Using AI Learning and IoT-Based Model

A review of understanding crop disease identification and classification using artificial intelligence and IOT-based models for driving learning approaches. AI-based unsupervised algorithms, Convolution Neural Networks (CNN), and image processing methods facilitate and are used in soybean plants for validations of germination and health. Further, various techniques exist to identify different diseases and help pre-alarm the farmers to reduce agriculture waste and resources. The AI methods are appropriate for improving the production cycle and yielding high-quality-based production. The technologies are focused on the stepwise farming of soybean crops. This review suggested enhancement ideology for soybean crop germination and prevention of diseases. Specifically, reviews concentrate on integrating the deep learning algorithm, Residual Convolution Neural Network (ResCNN), to monitor plant condition and growth from leaf to root. This review suggested various developments and uses of IOT-based robotic sensors for image classification of Soybean crops and proposed the best method for disease identification. The review facilitates the deployment of an AI learning called ResCNN for segmenting the images where the classification predicts the plant health, and plant root conditions with suggestions for the prevention steps for the growth of plants.