Comprehensive analysis of corn and maize plant disease detection and control using various machine learning algorithms and Internet of things - Varun Prakash R.; Kirubakaran G. (2023)
In today's world, Internet of Things (IoT) technology has revolutionized various fields – including agriculture – by offering smart solutions that enhance crop yield and quality. One crucial aspect of smart farming is the application of IoT for disease detection and prevention. Crop diseases caused by bacteria and viruses can have devastating effects, leading to significant financial losses for farmers. Early detection plays a crucial role in managing these diseases, and IoT technology provides reliable and efficient methods to achieve this goal. To aid in the research on disease recognition in corn or maize plants, a large-scale dataset comprising 4188 images across four disease categories – namely, healthy, common rust, gray-leaf spot, and blight – is utilized. This dataset is used to train various machine learning algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), decision tree (DT), convolutional neural network (CNN), naïve Bayes (NB), gradient boosting trees (GBT), long short-term memory (LSTM), and the algorithms from recent literature such as genetic algorithm-based ensemble classifier, XGBoost, AdaBoost and XGBoost + KNN to accurately identify disease patterns in plants and is loaded into the Raspberry Pi. By interfacing a camera with a Raspberry Pi, leaf diseases can be detected, and the farm’s status can be sent to the farmer’s webpage using a WiFi server. This information empowers farmers to take immediate action to control the disease such as activating sprinklers or applying pesticides. Through manually controllable on/off mechanisms, farmers can remotely manage these actions, ensuring the health and disease-free state of their crops. Among the selected algorithms, the XGBoost + KNN algorithm demonstrates the highest accuracy, reaching an impressive 98.77%.
Keywords: Corn -- Diseases and pests -- Control; Machine learning