AI-Enabled IoT for Precision Agriculture and Smart Crop Management
TL;DR
Overall, the reviewed material indicates that integrating IoT sensing with AI/ML can support real-time monitoring, resource optimization and faster agricultural decisions, however, Internet dependence, cybersecurity, system complexity, adoption cost, limited datasets and reduced accuracy for visually similar crop diseases remain important challenges.
Abstract
Agriculture is increasingly dependent on technologies that can improve productivity while reducing the consumption of water, fertilizers, pesticides, herbicides, labour and other resources. The five source chapters reviewed for this paper collectively describe the role of Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), computer vision, cloud computing and embedded systems in smart farming. The summarized work focuses on continuous sensing of soil and environmental parameters, automated irrigation, crop and weed monitoring, disease identification, yield prediction and decision support. The sources identify Artificial Neural Networks (ANNs), Deep Learning, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) as important approaches. A prototype architecture is also described using Arduino Mega 2560, Raspberry Pi, multiple sensors, Firebase and an Apache web server. In the disease-detection prototype, 295 leaf images were divided into training, validation and testing groups, and a CNN-based mobile application produced a reported confidence score of 0.97 for an example operation with an execution time of approximately 0.88 seconds. Overall, the reviewed material indicates that integrating IoT sensing with AI/ML can support real-time monitoring, resource optimization and faster agricultural decisions. However, Internet dependence, cybersecurity, system complexity, adoption cost, limited datasets and reduced accuracy for visually similar crop diseases remain important challenges.