- Review
Uniform application of fertilizers and pesticides continues to dominate global agriculture despite significant spatial variability in soil and crop conditions. This mismatch results in avoidable yield gaps, excessive chemical waste, and environmental pressures, including nutrient leaching and greenhouse gas emissions. The integration of Artificial Intelligence (AI) and Remote Sensing (RS) has emerged as a transformative framework for diagnosing this variability and enabling site-specific, climate-responsive management. This systematic synthesis reviews evidence from 2000–2025 to assess how AI–RS technologies optimize agrochemical efficiency. A comprehensive search across Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar were used. Following rigorous screening and quality assessment, 142 studies were selected for detailed analysis. Data extraction focused on sensor platforms (Landsat-8/9, Sentinel-1/2, UAVs), AI approaches (Random Forests, CNNs, Physics-Informed Neural Networks), and operational outcomes. The synthesized data demonstrate that AI–RS systems can predict critical soil attributes, specifically salinity, moisture, and nutrient levels, with 80–97% accuracy in some cases, depending on spectral resolution and algorithm choice. Operational implementations of Variable-Rate Application (VRA) guided by these predictive maps resulted in fertilizer reductions of 15–30%, pesticide use reductions of 20–40%, and improvements in water-use efficiency of 25–40%. In fields with high soil heterogeneity, these precision strategies delivered yield gains of 8–15%. AI–RS technologies have matured from experimental methods into robust tools capable of shifting agrochemical science from reactive, uniform practices to predictive, precise strategies. However, widespread adoption is currently limited by challenges in data standardization, model transferability, and regulatory alignment. Future progress requires the development of interoperable data infrastructures, digital soil twins, and multi-sensor fusion pipelines to position these technologies as central pillars of sustainable agricultural intensification.
20 December 2025




![FJB staining in the rat hippocampus and dorsal thalamus demonstrated neuroprotective effects of ILE in the treatment of OP poisoning. FJB-stained neurons appear as bright green dots under fluorescence microscopy. Bright FJB fluorescence in neurons indicates serious neuronal damage. No neuronal FJB staining was observed in the uninjured control brains (top panels). In the animals given paraoxon (POX) but not treated with ILE (ISO), neuronal FJB staining in the hippocampus was observed in pyramidal cells in CA1, CA2, and CA3 of the hippocampus, as well as in neurons in the polymorph layer ((center left panel); cc = corpus callosum). No neuronal FJB staining was observed in the hippocampus of any of the animals treated with ILE 30 min after paraoxon (bottom left panel). The dorsal thalamus was another site of extensive neuronal FJB staining in the animals given paraoxon but not treated with the ILE ((center right panel); sm = stria medullaris). Very minimal neuronal FJB staining was observed in the dorsal thalamus of all of the ILE-treated animals (bottom right panel). Figure reprinted with permission from [49].](https://mdpi-res.com/agrochemicals/agrochemicals-04-00022/article_deploy/html/images/agrochemicals-04-00022-ag-550.jpg)
