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

• Published in: IEEE access Vol. 10; pp. 41525 - 41536
• Main Authors: Sarma, Kandarpa Kumar, Das, Kunal Kingkar, Mishra, Vikash, Bhuiya, Samadrita, Kaplun, Dmitrii
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agriculture; Artificial intelligence; Artificial neural networks; Clustering; CNN; Convolutional neural networks; Decision support systems; Diseases; Fractal analysis; Image processing; Image segmentation; Infrared imagery; Internet of Things; leaf disease; Moisture effects; Monitoring; near-infrared images; Plant diseases; Process control; smart agriculture; Soil moisture; Support vector machines; Vegetation; Vegetation mapping
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3167061

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