Artificial intelligence has moved from a peripheral research interest to a central pillar of modern crop and livestock production. This review synthesises recent peer-reviewed literature on the application of artificial intelligence across the agricultural value chain, covering precision farming, crop disease and pest detection, yield forecasting, weed management, agricultural robotics, precision livestock farming, and Internet of Things-enabled resource management. The review also considers the growing use of explainable artificial intelligence and the bibliometric patterns that characterise this rapidly expanding field. Machine learning and deep learning techniques, particularly convolutional neural networks, have delivered measurable gains in disease classification accuracy, yield estimation, and autonomous field operations, while remaining constrained by data scarcity, poor cross-environmental generalisation, and limited interpretability. Precision livestock farming has extended these gains to animal welfare monitoring and reproductive management, and Internet of Things architectures have provided the sensing backbone that links artificial intelligence models to real-time field conditions. Despite substantial technical progress, adoption remains uneven, particularly among smallholder farmers in low- and middle-income regions, owing to infrastructure gaps, cost, and limited digital literacy. The review concludes that artificial intelligence in agriculture has reached a stage of technical maturity in controlled settings but requires further work on model transparency, data standardisation, and equitable deployment before its benefits can be realised at scale. Future research should prioritise lightweight and edge-deployable models, federated and privacy-preserving learning architectures, and closer integration between agronomic domain knowledge and algorithmic design.
H. Saharan, Aditi Mathur, Anubhav Beniwal et al.· Journal of Scientific Resear...· 0 citations
This research proposes a new framework of analysis in predicting high-impact deviations in an environmental system in changing observational circumstances. The new approach, which is called Distribution-adaptive Uncertainty Synthesis (DAUS) is made to work without explicit physical assumptions or time-order-dependence, addressing instead latent structure regularities in the heterogeneous observations. DAUS combines regime invariant encoding and uncertainty resilient optimization to align with sub-surface distributional instability which is a precursor to extreme behavior of a system. The structure utilizes the dual-channel latent representations in maintaining variability and structural consistency and adaptive uncertainty synthesis mechanism dynamically increases signals linked to high deviation potential. A self-recalibration thresholding approach also allows the end-on continuous recalibration of non-stationary input distributions. In comparison to traditional predictive structures, DAUS is based on anticipatory sensitivity instead of having point estimation accuracy, enabling it to be practical in case of sudden regime changes and partial information. Experimental assessment on various benchmark data proves that the suggested strategy always yields superior results compared to current strategies in detecting high-deviation cases, especially those in the cases of distributional volatility and the presence of noise. These findings suggest that DAUS is a good and generalizable route to further study of the environmental system and has significant potentials of being integrated into decision-support pipelines where the uncertainty awareness and adaptive responsiveness is essential. The suggested technique attains an overall accuracy of roughly 91.6%, indicating its robust and equitable performance across detection reliability metrics.
Dharavath Nagesh, P. Deepthi, A. Sahu et al.· 2026 5th OPJU International...· 0 citations