Chapter 3: AI in Smart Agriculture and Conservation
Synopsis
Author
Mrs. Hertaraise Mounica Khandavalli,
Assistant Professor, Department of Humanities & Basic Sciences, Sir C.R. Reddy College of Pharmaceutical Sciences, Eluru, Andhra Pradesh, India
Abstract
Smart agriculture harnesses artificial intelligence to optimize plant growth conditions through the meticulous analysis of soil and climate data, transforming agronomy from a reactive practice into a precise, predictive science. Machine learning models forecast crop yields by synthesizing complex environmental inputs from IoT sensor networks, enabling proactive resource management that strictly aligns water and nutrient delivery with the exact biological requirements of the crop. Computer vision systems, utilizing high-resolution drone and satellite imagery, enable real-time monitoring of plant health, detecting subtle signs of water stress and pest infestations via hyperspectral analysis before they become visible to the human eye. In the field of controlled environment agriculture, AI functions as the central nervous system for greenhouse automation, dynamically regulating climate, lighting, and irrigation systems to maintain ideal vapor pressure deficits and maximize photosynthetic efficiency. Robotics, guided by advanced computer vision and soft manipulation, automate delicate planting and harvesting tasks within vertical farms, ensuring consistency and reducing labor reliance. Beyond production, conservation efforts for medicinal plants leverage Geographic Information Systems (GIS) and remote sensing to map fragmented habitats and identify critical biodiversity hotspots threatened by anthropogenic activity. Predictive modeling rigorously assesses the impact of climate change on the geographic distribution of these vulnerable species, guiding assisted migration strategies to preserve genetic diversity.
Keywords: Precision Agriculture, Hyperspectral Imaging, Model Predictive Control (MPC), Vertical Farming Robotics, Species Distribution Modeling, Digital Twins
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