Learning Aided System for Agriculture Monitoring Designed Using Image Processing and IoT-CNN

The Internet of Things (IoT) and artificial intelligence (AI) based methods for monitoring, control, decision support are combined to design a smart agriculture assistance system. proposed system has sensor pack that provides continuous data capture temperature records, air soil moisture camera obtaining near-infrared (NIR) images the plant leaves use with an AI We identify twelve types vegetation study, out which five disease classes tomato categorized using Convolutional Neural Network (CNN). work also includes experiments conducted multiple clustering-based segmentation some features namely Gray level co-occurrence matrix (GLCM), Local binary pattern (LBP), Binary Level Co-occurrence Matrix (LBGLCM), Run Length (GLRLM), Segmentation-based Fractal Texture Analysis (SFTA). Out several tools, CNN proves be effective in providing automated classifying leaf through cloud server can accessed app. Extensive on-field trials show (VGG16 CNN, GLCM fuzzy clustering) is hot humid conditions reliable identifying certain vegetable types, usable cover farmland regulation watering mechanism crops.