Smart IoT-Enabled Deep Learning for Diagnosing Maize Leaf Diseases

As a technology that will help alter agriculture, Convolutional Neural Networks (CNN) and the Internet of Things (IoT) are being integrated more and more by researchers. With the use of IoT, farmers will be able to make decisions and take action based on data about field conditions obtained from sensor nodes somewhat than only on their experience, which will reduce the amount of resource waste. However, CNN augments monitoring systems with duties like crop disease early detection or estimating the amount of useable means and supplies (water, manures) essential to rise yield. In order to observe environmental and physical characteristics, enable initial disease identification, and support precision agriculture, this study presents an IoT and CNN-based technology platform. The pretrained model used in this platform is the Inception v3 since it largely concentrates on using less processing power by altering the earlier Inception architectures. The IoT and CNN-based technology platform successfully identified early signs of crop diseases with an accuracy rate of 92%, compared to 75% with traditional methods. This improvement significantly reduces the time required for disease diagnosis and intervention. The study demonstrates that the IoT and CNN-based technology platform not only enhances early detection of crop diseases but also optimizes resource usage and improves yield prediction accuracy.