Pesticide Prediction and Disease Identification with AIoT

Agriculture is vital to human survival and has a significant impact on the economy of any nation. Crop protection costs millions of dollars per year. Insects and other pests pose a serious threat to the health of a harvest. Excessive use of chemical fertilizers and pesticides negatively affects the crop and soil quality. Therefore, one way to safeguard the harvest and mitigate potential losses is through early identification of the pests. Examining the crop at the right moment is the best technique to determine its overall health. While manual inspection is the standard way of conducting field inspection, it becomes challenging for large fields. In addition, manual inspection would be exceedingly expensive and tedious. To address this, an automated system is needed to detect pests, identify them, and recommend appropriate fertilizers using an IoT system. Therefore, automated pest detection has become a major focus for researchers globally, as it offers a more efficient and cost-effective alternative to manual inspection. In this work, a smart agriculture system has been proposed that monitors crops, identifies pests, and allows remote control. The dataset comprises over 4000 images of corn leaves, categorized into rust, blight, grey spots, and healthy leaves. By employing Convolutional Neural Networks (CNN), the system has achieved a remarkable 99% accuracy in pest detection.

Keywords: Agriculture, Automation, Convolutional Neural Network (CNN), Crop protection, IOT system, Irrigation, Image processing, Pest infestation, Pest identification.