Nitrogen responses vary under diverse agronomic management practices, influencing vegetation indices (VIs) and productivity across different ecological conditions. However, the proper quantification of these responses under various crop establishment methods with varied nitrogen levels is rarely studied. Therefore, a field experiment was conducted to investigate the impact of varying nitrogen levels on VIs, growth parameters, yield attributes, yield, and economic aspects of transplanted rice (TR) and direct-seeded rice (DSR). The experiment was conducted in the randomized block design consisted seven N levels, which included 0% recommended dose of nitrogen (RDN) or no nitrogen (N₀), 33.33% RDN (N₁), 66.66% RDN (N₂), 100% RDN (N₃), 133.33% RDN (N₄), 166.66% RDN (N₅) and 200% RDN (N₆), and replicated thrice. The plots with higher N levels demonstrated increased values of VIs and treatment N₃ (120 kg N ha⁻¹), N₄ (160 kg N ha⁻¹), N₅ (200 kg N ha⁻¹), and N₆ (240 kg N ha⁻¹) showed no statistically significant differences in NDVI (normalized difference vegetation index), RVI (ratio vegetation index), NDRE (normalized difference red edge), and GNDVI (green normalized difference vegetation index) values across the various growth stages of rice. The application of treatment N₄ resulted in the highest number of panicles m⁻² (348.2 in TR, 376.8 in DSR), filled grains panicle⁻¹ (74.55 in TR, 62.43 in DSR), and a 1,000-grain weight of 26.92 g in TR and 26.76 g in DSR. The maximum yield (4.89 t ha⁻¹) was obtained in transplanted rice at treatment N₄ and, 8.15% yield reduction was noted in DSR for the same treatment, which was statistically equivalent to N₃, but significantly superior to other N levels. Conversely, in DSR with RDN (120 kg N ha⁻¹), the cost–benefit ratio surpassed that of TR by 16.96%, signifying DSR’s adaptability for more profitable rice cultivation in the region. This research provides valuable insights into optimizing nitrogen management practices for TR and DSR, thereby enhancing rice crop performance and economic returns.

The development of an efficient electronic metering system is crucial for the successful deployment of urea super granules (USGs) in agricultural fields. Key parameters influencing the performance of the cell-type metering system for USG application include cell area, peripheral speed of the cell, and the level of hopper filling. Simulation of the metering system operation was conducted using EDEM, with a focus on optimizing input parameters to achieve 100% cell fill, maximum qualified rate for single cells, and minimization of missing or multiple cells. An electronic metering system was implemented in a soil bin, employing a Nema 23 stepper motor to control the peripheral speed of the cell. The synchronization with the transplanter was ensured through a rotary encoder, and various cell sizes were achieved using 3D printing. Using the EDEM-RSM approach, the optimal operating conditions based on the soil bin study were found as cell area of 1088 mm ² , a metering roller peripheral speed of 0.24 m/s, and a 75% hopper fill. Under these conditions, the system demonstrated a 97% cell fill, a 91% qualified rate for single cells, with only 3.2% missing cells and 4.5% multiple cells. Quality class of metering was found as good based on indices such as missing index, multiple index, and quality of feed index, which were found as 3.2%, 4.5%, and 92.3%, respectively. The study demonstrated the robust predictive capability of the EDEM-RSM model in evaluating the performance of the cell-type metering system. These findings offer valuable insights for researchers and manufacturers seeking to optimize the development of automated ultrasonic (USG) applicators.

Brinjal (Solanum melongena L.) is an important vegetable crop worldwide, but its cultivation faces challenges from pests, diseases, and variable environmental conditions that negatively affect quality and yield. Accurate fruit detection in natural field conditions is essential for yield estimation and perception module of automated harvesting, but existing deep learning techniques often require substantial computational support, restricting their deployment on edge devices. This study addresses this gap by evaluating the Faster Objects, More Objects (FOMO) model—a lightweight architecture for resource-constrained platforms—for brinjal detection under diverse field conditions. A dataset of 1500 images was captured under varying illumination and growth stages, annotated using a bounding box-based approach, and used to train FOMO models with 25, 50, and 100 epochs via transfer learning. Post-training quantization to INT8 format was applied to assess improvements in computational efficiency. The Float32 model achieved a precision of 0.827, recall of 0.915, and F1-score of 0.869 at 100 epochs. The INT8-quantized model maintained comparable accuracy (precision 0.829, recall 0.908, F1-score 0.866) while reducing model size by 63.33% (from 0.30 MB to 0.11 MB), inference time by 62.02% (from 65.3 ms to 24.8 ms), and RAM usage by 73.02% (from 887.2 KB to 239.4 KB). These results demonstrate that FOMO combined with INT8 quantization provides an efficient, accurate solution for real-time brinjal detection on edge platforms, supporting the advancement of precision agriculture through intelligent crop monitoring and serving as a perception module for future robotic harvesting systems.