The recent advances in the Artificial Intelligence have accelerated the growth process and agriculture is no exception. A number of AI based applications are knocking the door and ready to take the world by storm. However, AI/ML applications in Agriculture will hardly be of any pragmatic use unless it is supported by spatial database. Geospatial technology
is the best tool to capture large scale data set at varied spatiotemporal resolutions and lays the foundation for transforming modern agriculture by enabling precision farming, real time monitoring and data driven decision making for sustainable resource management. This review presents an overview of recent advances in geospatial technologies and their applications in agriculture, with particular emphasis on the Indian context. The study discusses the role of remote sensing, Geographic Information Systems (GIS), unmanned aerial vehicles (UAVs), artificial intelligence (AI), machine learning (ML), cloud computing and geospatial analytics in agricultural monitoring and management. The integration of multi-source Earth Observation data from satellite missions such as Landsat, Sentinel, MODIS, Resources at, RISAT and emerging hyperspectral and thermal missions has significantly improved crop monitoring, yield prediction, digital soil mapping, disease and pest detection, soil moisture assessment and agricultural water management. AI and ML based approaches have enhanced the accuracy and efficiency of geospatial analyses,
enabling predictive and automated agricultural applications. The review also highlights major challenges, including data quality, cloud contamination, limited ground truth observations, model interpretability and scalability for smallholder farming systems. Furthermore, ongoing government initiatives such as AgriStack, PMFBY, FASAL, CHAMAN and KrishiDSS are discussed in the context of digital agriculture development in India. Future trends indicate a transition toward UAV based real time farm monitoring, digital twins and climate resilient predictive farming systems, supporting sustainable agricultural production, improved resource use efficiency and enhanced food security.
Optimizing nitrogen (N) use efficiency in rice requires effective management of crop–weed competition. A field experiment was conducted during the Kharif season of 2018 in SKUAST Kashmir to evaluate the interactive effects of nitrogen levels (0, 45, and 90 kg N ha¹) and weed management practices on weed dynamics, nutrient partitioning, resource use efficiency, yield, and grain quality of basmati rice (Shalimar Sugandh 1). Weed interference significantly reduced grain yield (3.89 t ha¹), crop nutrient uptake, and resource use efficiency, accounting for 11.78% nutrient loss to weeds. In contrast, application of penoxsulam @ 22.5 g ha¹ effectively reduced weed biomass (16.74 g m²), improved weed control efficiency (72.08%), and enhanced grain yield (6.46 t ha¹), comparable to weed free conditions. Nitrogen application increased grain yield up to 6.49 t ha ¹, with an agronomic efficiency of 17.55 kg grain kg¹ N at 90 kg N ha¹. Improved weed control significantly enhanced crop nutrient advantage and reduced nutrient losses, indicating a strong shift in resource capture towards the crop. Grain quality parameters remained largely stable across treatments, although gel consistency improved under higher nitrogen and effective weed control, suggesting enhanced starch deposition. The results highlight that maximizing nitrogen use efficiency and sustaining grain quality in basmati rice requires integrated nutrient and weed management, particularly early postemergence herbicide application to minimize competitive losses.
Brown top millet (Brachiaria ramosa L.) is an underutilized, climate resilient cereal with considerable potential for enhancing food, nutritional, and fodder security in rainfed production systems. However, information on the performance of improved genotypes under the rainfed upland ecology of the Bastar Plateau of central India remains limited. The present study evaluated eight advanced genotypes and one local check during Kharif 2025 at Shaheed Gundadhur College of Agriculture and Research Station, Jagdalpur, using a randomized block design with three replications under natural rainfall conditions. Significant (P < 0.05) genotypic differences were observed for phenological, morphological, and productivity related traits, indicating substantial genetic variability for selection. Days to 50% flowering ranged from 57 to 59 days, while maturity varied from 88 to 90 days. Grain yield ranged from 910 to 1418 kg ha¹, and fodder yield from 3704 to 4660 kg ha¹. TNBr 018 recorded the highest grain yield (1418 kg ha¹), representing a 48.3% yield advantage over the local check, whereas TNBr 017 produced the highest fodder yield (4660 kg ha¹) while maintaining high grain productivity (1356 kg ha¹). The favourable seasonal rainfall (1476.1 mm over 68 rainy days) enabled reliable assessment of genotype performance under representative rainfed conditions. Based on overall agronomic performance, TNBr 018 was identified as the most promising genotype for grain production, whereas TNBr 017 showed superior dual-purpose potential. These genotypes warrant further multi location evaluation and may serve as valuable genetic resources for developing climate resilient brown top millet cultivars for rainfed agroecosystems.
Litchi places among the most important subtropical fruit with significant commercial value due to its attractive colour and nutritional richness. Enhancement in fruit quality has consistently been a major objective to improve its marketability. In the recent field experiment, the effect of foliar spray of nano fertilizers on improving the quality of litchi fruits was evaluated. A total of three concentrations (50 ppm, 100 ppm and 200 ppm) of four different nano fertilizers; silicon dioxide (SiO2), nano zinc oxide (ZnO), nano urea and nano potassium (K) along with control (water) was taken. The foliar application of all the nano fertilizers was done thrice; first during the pea stage, followed by second and third sprays after 15 days and 30 days, respectively. Among all the treatments, application of nano K at 50 ppm resulted in higher fruit and seed size, increased pulp and peel weight in contrast to control. The foliar application of nano ZnO at 100 ppm significantly improved total soluble solids (TSS), total sugars and also reduced acidity at the same time. The present study revealed that preharvest application of nano K (50 ppm) and ZnO at 100 ppm significantly improved the quality of litchi fruits. The results indicated new perspective for sustainable cultivation of litchi with the application of nano fertilizers.
The present study was carried out during 2023–24 at GBPUAT, Pantnagar, Uttarakhand. The study was conducted to evaluate the effect of ethrel on ripening, fruit quality, antioxidant properties and shelf life of sapota fruits. The experiment was conducted in a factorial Completely Randomized Design (CRD) with seven treatments and three replications. Mature fruits of sapota cv. Cricket Ball were treated with different concentrations of ethrel (500, 1000, 1500, 2000 and 2500 ppm), calcium carbide @ 10 g kg¹ fruits, untreated control and stored under ambient conditions. Observations were recorded at 3, 6, 9 and 12 days after treatment. Significant differences were observed among treatments for physiological loss in weight, firmness, total soluble solids, total sugars, ascorbic acid, total phenolic content, total antioxidant activity and shelf life. Fruits treated with
Ethrel @ 1000 ppm showed more uniform ripening, higher TSS and total sugars, acceptable firmness, better antioxidant retention. The higher shelf life was recorded with ethrel @ 500 ppm (10.50 days), which was statistically at par with ethrel @ 1000 ppm
(10.33 days). However, CaC2 treated fruits showed rapid deterioration, poor quality retention and lowest shelf life. Overall, ethrel @ 1000 ppm provided a favourable balance between ripening uniformity, TSS and sugar accumulation, antioxidant retention
and acceptable shelf life under the ambient storage conditions of the study.
