Predicting crop yield at preharvest is pivotal for agricultural policy and strategic decision making. Despite global agricultural targets, labour-intensive surveys for yield estimation pose challenges. Using unmanned aerial vehicle (UAV)-based multispectral sensors, this study assessed crop phenology and biotic stress conditions using various spectral vegetation indices. The goal was to enhance the accuracy of predicting key agricultural parameters, such as leaf area index (LAI), soil and plant analyser development (SPAD) chlorophyll, and grain yield of maize. The study’s findings demonstrate that during the kharif season, the wide dynamic range vegetation index (WDRVI) showcased superior correlation coefficients (R), coefficients of determination (R²), and the lowest root mean square errors (RMSEs) of 0.92, 0.86, and 0.14, respectively. However, during the rabi season, the atmospherically resistant vegetation index (ARVI) achieved the highest R and R² and the lowest RMSEs of 0.83, 0.79, and 0.15, respectively, indicating better accuracy in predicting LAI. Conversely, the normalised difference red-edge index (NDRE) during the kharif season and the modified chlorophyll absorption ratio index (MCARI) during the rabi season were identified as the predictors with the highest accuracy for SPAD chlorophyll prediction. Specifically, R values of 0.91 and 0.94, R² values of 0.83 and 0.82, and RMSE values of 2.07 and 3.10 were obtained, respectively. The most effective indices for LAI prediction during the kharif season (WDRVI and NDRE) and for SPAD chlorophyll prediction during the rabi season (ARVI and MCARI) were further utilised to construct a yield model using stepwise regression analysis. Integrating the predicted LAI and SPAD chlorophyll values into the model resulted in higher accuracy compared to individual predictions. More exactly, the R² values were 0.51 and 0.74, while the RMSE values were 9.25 and 6.72, during the kharif and rabi seasons, respectively. These findings underscore the utility of UAV-based multispectral imaging in predicting crop yields, thereby aiding in sustainable crop management practices and benefiting farmers and policymakers alike.

Mulching techniques that are adaptive and extremely successful may be able to reduce the detrimental impacts of crop production and water stress on different varieties of peas in a climate change scenario. Field experiment was carried out in the Umroi region of Meghalaya during the rabi season (2020–2021) for this reason. This study used a split-plot design with three organic mulches as the main plot treatment and four pea types as the sub-plot treatments, which was replicated three times. The field trial showed that paddy straw mulch significantly outperformed both weed mulch and no mulch in terms of green pod yield, water productivity, and benefit cost ratio, with values of 89.33, 54.14 kg ha^-1 mm^-1, and 3.16, respectively.VM 12 greatly topped other cultivars in terms of green pod yield, water productivity, and benefit-cost ratio (BCR), with values of 89.78 kg ha^-1, 54.41 kg ha^-1 mm^-1 and 3.33, respectively. The investigation showed that the best alternative agronomic strategy for achieving the maximum yield output of garden pea was paddy straw mulch, followed by weed mulch and un-mulch.