To enable rapid, non-destructive analysis and functional classification of karst calcareous soils in Shilin County, Kunming, this study acquired mid-infrared attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectra in the 4000-900\cmi{} range from 409 soil samples. After standardized preprocessing, the samples were preliminarily partitioned into four spectral groups by K-means clustering, and a partial least squares discriminant analysis (PLS-DA) model was constructed. Variable importance in projection (VIP) scores were further used to identify key spectral features distinguishing the classes. The spectra were dominated by peaks at 3620, 1650, and 1020\cmi{}, and no obvious... More >

Open-field vegetable production in the cold plateau regions of Northwest China faces labor shortages, extensive management, meteorological risks, and inefficient use of agricultural inputs. Taking a 6.67 ha smart farm in Huangzhong District, Xining, Qinghai Province, China, as a case study, this study developed a four-tier smart agriculture system comprising perception, transmission, platform, and application layers. The system integrates agricultural Internet of Things (IoT) monitoring, BeiDou Navigation Satellite System (BDS)-based intelligent machinery, precision water and fertilizer management, and a big-data decision-making platform, all adapted to plateau conditions. Field trials and o... More >

Low-productivity agricultural belts in India face the compounding realities of land fragmentation, erratic weather, and heterogeneous soils, which together limit harvest outcomes. Precision agriculture and big data analytics together offer an innovative solution that turns large volumes of heterogeneous data into actionable agronomic intelligence. This study proposes a big-data-driven crop yield prediction (CYP) framework tailored to low-productivity districts in India. The architecture combines soil and climatic analytics with data warehousing, machine learning (four base regressors and voting-based hybrid ensembles), and a preprocessing pipeline designed for compatibility with distributed... More >

Agriculture faces growing pressures from food insecurity, climate change, and resource scarcity, increasing demand for scalable analytical tools. This PRISMA 2020-compliant systematic review synthesised 582 peer-reviewed studies on machine learning (ML) and deep learning (DL) in agriculture published between January 2019 and March 2026, with 430 retrieved full texts forming the analytic subsample. Publication volume increased from 6 papers in 2019 to 251 in 2025, corresponding to a compound annual growth of approximately 86%. Convolutional backbones remained dominant (57.2%), while Transformer-based models increased from 14.3% in 2022 to 41.2% in 2025. South and East Asia contributed 59.3% o... More >

Efficient water and nutrient management remains a major challenge in modern agriculture due to rising fertilizer costs, global water scarcity, and environmental concerns such as groundwater contamination from nutrient leaching. Conventional fertigation typically relies on uniform application rates that neglect spatial and temporal root-zone variability, resulting in inefficient resource utilization. To address these limitations, this study proposes and evaluates an automated Multi-Variable Hybrid Predictive Control (HPC) framework for precision fertigation. The system captures the highly nonlinear dynamics of rapid soil moisture changes and slower nutrient transport using a multi-variable st... More >