Precision agronomy in semi-arid regions: Role of GIS, remote sensing and AI in crop management

Abstract:

Semi-arid regions occupy a significant portion of the global agricultural landscape and are characterized by low and erratic rainfall, high evapotranspiration, and fragile soil conditions that limit stable crop productivity (FAO, 2021) ^[3]. Agricultural production in these areas is frequently constrained by soil degradation, nutrient depletion, salinity problems, and poor water-use efficiency, which ultimately reduce crop yield potential (Lal, 2020) ^[6]. Climate variability, increasing temperature, and uncertain precipitation patterns further intensify production risks, making traditional agronomic practices less reliable for ensuring sustainable food production (IPCC, 2022) ^[5]. Therefore, advanced technological interventions are required to enhance resource-use efficiency and maintain productivity under moisture-limited environments (Rockström et al., 2017) ^[9]. Precision agronomy has emerged as a modern crop management approach that uses advanced technologies to manage spatial and temporal variability within agricultural fields (Gebbers & Adamchuk, 2019) ^[4]. Geographic Information System (GIS) technology plays an important role in collecting, storing, analyzing, and visualizing spatial information related to soil characteristics, crop growth variability, and land suitability (Burrough & McDonnell, 2018) ^{[22, 2]}. Remote sensing techniques provide continuous and large-scale monitoring of crop conditions, vegetation indices, soil moisture content, and drought stress using satellite imagery and drone-based sensors (Thenkabail, 2018) ^[11]. Artificial Intelligence (AI) methods, including machine learning and deep learning algorithms, enable accurate prediction of crop growth, yield estimation, disease detection, and decision-making support systems (Liakos et al., 2018) ^[7]. Integration of GIS, remote sensing, and AI technologies helps develop site-specific crop management strategies such as precision irrigation scheduling, variable rate fertilizer application, and early stress detection (Zhang et al., 2021) ^[13]. Remote sensing-based vegetation indices such as NDVI are widely used to assess crop vigor, biomass accumulation, and spatial variability in crop performance across semi-arid regions (Rouse et al., 1974) ^[10]. Big data analytics, IoT sensors, and UAV technologies further enhance the efficiency and accuracy of precision agriculture systems (Wolfert et al., 2017) ^[12].This review highlights the role of GIS, remote sensing, and AI in improving crop monitoring, soil resource management, water-use efficiency, and climate-resilient agriculture in semi-arid regions (Pierce & Nowak, 2019) ^[8]. The study also discusses technological challenges, research gaps, and future opportunities for sustainable crop production using precision agronomy tools (Bongiovanni & Lowenberg-Deboer, 2018) ^[1].