Precision and Digital Farming Reconsidered: A Critical Narrative Review of Sensors, Artificial Intelligence and Remote Sensing in Crop Production
Syed Shujat Hussain *
Krishi Viygan Kendra Pulwama, Sheri Kashmir University of Science and Technology, Kashmir, India.
Ajaz Ahmad Ganie
Krishi Viygan Kendra Pulwama, Sheri Kashmir University of Science and Technology, Kashmir, India.
Mohammad Saleem Dar
Krishi Viygan Kendra Pulwama, Sheri Kashmir University of Science and Technology, Kashmir, India.
Gowher Nabi Parrey
Krishi Viygan Kendra Pulwama, Sheri Kashmir University of Science and Technology, Kashmir, India.
Javeed Ahmed Mugloo
Krishi Viygan Kendra Pulwama, Sheri Kashmir University of Science and Technology, Kashmir, India.
Owais Ahmad Wagay
Krishi Viygan Kendra Pulwama, Sheri Kashmir University of Science and Technology, Kashmir, India.
*Author to whom correspondence should be addressed.
Abstract
Sensing hardware, satellite and unmanned aerial observation, and machine learning are now routinely presented as the technological foundation of a more productive and less environmentally damaging agriculture. The scholarly literature supporting this position has expanded rapidly, yet it remains fragmented across agronomy, remote sensing, computer science, agricultural economics and rural sociology, and the strength of the underlying evidence is uneven. This review examines the state of knowledge on sensing, artificial intelligence and remote sensing in arable and horticultural crop production, and evaluates how far demonstrated technical performance has translated into demonstrated agronomic, economic and environmental benefit. Literature was identified through structured searching of open bibliographic indexes and scholarly search platforms, supplemented by citation searching of recent reviews and by targeted retrieval of institutional reports, with every retained source verified against the digital object identifier registry. The synthesis is organised around four analytical layers: measurement, interpretation, decision and actuation. The evidence is strongest for measurement, where sensor and platform performance has been characterised extensively, and weakest at the point where measurements become agronomic decisions. Machine learning models frequently achieve high accuracy under conditions closely resembling their training data, but independent evaluation across sites, seasons and cultivars is uncommon, and reported accuracies are therefore poor predictors of operational value. Field evidence for variable-rate nutrient management and for autonomous weeding is more heterogeneous than technology-focused reviews imply, and environmental claims frequently rest on short-duration trials concentrated in industrialised temperate systems. Persistent constraints on adoption are institutional and distributional rather than technical, involving data governance, trust, interoperability and unequal access to connectivity and ground data. The principal unresolved questions concern the transferability of models, the agronomic response functions on which prescriptions depend, and the conditions under which digital tools benefit smallholder systems.
Keywords: Precision agriculture, digital agriculture, proximal sensing, satellite remote sensing, machine learning, site-specific crop management, agricultural data governance