Fuente: PubMed "smart farming" Recent Adv Food Nutr Agric. 2026 Jun 8. doi: 10.2174/012772574X461759260530150948. Online ahead of print.ABSTRACTOBJECTIVE: Crop health enhancement plays a pivotal role in increasing crop growth and productivity. For sustained improvement in crop health, it is necessary to quantify crop health at successive stages of crop ontogeny. This study aims to quantify crop health across different stages of crop ontogeny using a mathematical model based on two main indicators: Soil moisture and soil quality.METHOD: A mathematical model, consisting of a composite generalized equation of soil moisture & soil quality, is developed. Data is collected from the digital platform for the evaluation and validation of the proposed model. Three iterations have been performed to obtain the optimal solution, and validation is carried out using the standard NDVI scale. The modelling framework enables the interpretation of crop health dynamics for the entire crop cycle.RESULTS: The results demonstrate that the proposed model can consistently quantify crop health. It provides timely, accurate insights into soil conditions. The calculated normalized value of the crop health in iteration 2 is 0.424, and in iteration 3 is 0.448, which fall within the third range (0.33-0.66) of the NDVI scale, reflecting moderate crop health.DISCUSSION: The theoretical implication of the study explores the influence of soil moisture & soil quality on crop health through the proposed mathematical model. This model will help farmers make more accurate decisions about crop conditions.. These results attest to the model's potential for application by small- and medium-scale farmers seeking to embrace evidence-based practices. The proposed mathematical modeling should be implemented with real-time data.CONCLUSION: The research contributes a novel composite mathematical modelling approach. It connects soil moisture and soil quality to a comprehensive quantification of crop health. It highlights a practical, scalable framework for advancing evidence-based agricultural practices.PMID:42261176 | DOI:10.2174/012772574X461759260530150948