The advent of high-spatial-resolution hyperspectral imagery from unmanned aerial vehicles (UAVs) made a breakthrough in the detailed retrieval of crop traits for precision crop-growth monitoring systems. Here, a hybrid approach of radiative transfer modelling combined with a machine learning (ML) algorithm is proposed for the retrieval of the leaf area index (LAI) and canopy chlorophyll content (CCC) of wheat cropland at the experimental farms of ICAR-Indian Agricultural Research Institute (IARI), New Delhi, India. A hyperspectral image captured from a UAV platform with spatial resolution of 4 cm and 269 spectral bands ranging from 400 to 1000 nm was processed for the retrieval of the LAI and CCC of wheat cropland. The radiative transfer model PROSAIL was used for simulating spectral data, and eight machine learning algorithms were evaluated for hybrid model development. The ML Gaussian process regression (GPR) algorithm was selected for the retrieval of crop traits due to its superior accuracy and lower associated uncertainty. Simulated spectra were sampled for training GPR models for LAI and CCC retrieval using dimensionality reduction and active learning techniques. LAI and CCC biophysical maps were generated from pre-processed hyperspectral data using trained GPR models and validated against in situ measurements, yielding R² values of 0.889 and 0.656, suggesting high retrieval accuracy. The normalised root mean square error (NRMSE) values reported for LAI and CCC retrieval are 8.579% and 14.842%, respectively. The study concludes with the development of optimized GPR models tailored for UAV-borne hyperspectral data for the near-real-time retrieval of wheat traits. This workflow can be upscaled to farmers’ fields, facilitating efficient crop monitoring and management.

Summary: Soil and nutrients losses due to soil erosion are detrimental to crop production, especially in the hilly terrains. An experiment was carried out in three consecutive cropping seasons (2012–2015) with four treatments: sole maize; sole maize with plastic mulch; maize and cowpea under plastic mulching; and maize and soybean under plastic mulching in randomized block design (RBD) to assess their impact on productivity, profitability, and resource (rainwater, soil, and NPK nutrients) conservation in the Indian sub-Himalayan region. The plot size was 9 × 8.1 m with 2% slope, and runoff and soil loss were measured using a multi-slot devisor. The results showed that mean runoff decreased from 356 mm in sole maize with plastic mulch plots to 229 mm in maize + cowpea intercropping with plastic mulch, representing a reduction of 36% and corresponding soil loss reduction was 41% (from 9.4 to 5.5 t ha⁻¹). The eroded soil exported a considerable amount of nitrogen (N) (13.2–31.4 kg ha⁻¹), phosphorous (P) (0.5–1.7 kg ha⁻¹), and potassium (K) (9.9–15.6 kg ha⁻¹) and was consistently lower in maize + cowpea intercropping. The maize equivalent yield (MEY) was significantly higher in maize + cowpea with plastic mulch intercropping than the other treatments. These results justify the need to adopt maize with alternate legume intercrops and plastic mulch. This strategy must be done in a way guaranteeing high yield stability to the smallholder farmers of the Indian sub-Himalayan region.

This study aimed to demonstrate the efficacy of drone-assisted crop monitoring in precision agriculture by evaluating the relationships between the NDVI, leaf area index (LAI), and leaf nitrogen content (LNC) in three wheat varieties (DBW-187, HD-3086, PBW-826) under eight nitrogen treatments (N0–N210). The NDVI was derived from drone-based multispectral imagery at the flowering (90 DAS) and grain-filling (108 DAS) stages. Strong correlations were observed between the NDVI, LAI, and LNC, with the R² values improving from 0.78–0.86 at flowering to 0.88–0.90 at grain filling. These findings highlight the potential of drone-derived indices for efficient crop monitoring, resource use optimization, and yield prediction in precision agriculture.