Ai In Smart Agriculture: A Review Of Ai-Based Soil Analysis And Fertilizer Management For Precision Agriculture
Year: 2026
Type: article
Abstract: Imbalanced fertilizer use is a major problem in Indian agriculture: excess nitrogen raises costs and pollutes water, while deficiencies of phosphorus, potassium and micronutrients limit yields. Effective fertilizer management requires knowing the nutrient status of each field, which conventional laboratory soil testing provides only slowly and at low spatial density. This paper reviews AI-based soil analysis and fertilizer management for precision agriculture. It examines soil sensing with in-field electrochemical NPK, pH and electrical-conductivity probes and with visible–near-infrared spectroscopy; machine learning models that classify soil fertility and predict nutrient levels from sensor and spectral data; digital soil mapping, which combines field samples with remote sensing and terrain covariates to map soil properties across landscapes; crop nitrogen status estimation; and recommendation engines that convert soil and crop information into site-specific fertilizer doses and variable-rate application. India's Soil Health Card programme is discussed as a data source and delivery channel. A five-layer architecture comprising soil sensing, data integration, AI analysis, fertilizer recommendation and farmer advisory layers is proposed, together with research gaps, objectives, a comparison of soil analysis methods and challenges such as sensor accuracy, sparse samples, model transferability, cost and farmer adoption. The review concludes that AI can make fertilizer use more efficient, profitable and environmentally sustainable.
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7225702556
FWCI:
Cites:
Cited by:
Related to: