Institute of Agricultural Machinery

The integration of solar photovoltaic (PV) systems with agricultural activities, known as rooftop agro-voltaic system (RTAVS), offers a promising approach to simultaneously meet energy and food production demands in urban and peri-urban areas. The study evaluates the energy performance and shading effect of a 76.8 kWp rooftop PV system installed at the CAET, DBSKKV, Dapoli, MH, India. The system comprises 240 mono-crystalline modules arranged in 12 strings and was monitored over two experimental periods (October 2022–May 2023 and October 2023–May 2024), covering winter and summer seasons. Energy performance was analyzed and shadow trajectory analysis was conducted using a 3D SketchUp model to simulate seasonal and daily shading patterns, quantifying shadow coverage over the cultivable area. Results demonstrated a strong correlation between solar irradiance and energy generation (R² = 0.984), indicating stable PV system performance under rooftop agro-voltaic conditions, with an estimated annual energy yield of 81,896 kWh. Seasonal shadow coverage ranged from 42.9% in winter to 18.9% in summer, with an overall mean of 30.9%. Moderate shading under RTAVS provided favorable conditions for shade-tolerant crops without compromising PV performance, confirming the technical feasibility of dual land use in urban renewable energy applications. This study presents a combined evaluation of PV performance and shadow trajectory in a rooftop agro-voltaic system, which has received limited attention in previous studies.

Methods for constructing complex flight trajectories of aircraft are analyzed, and the specific features of their formation are examined. A universal algorithm is proposed for generating an arbitrary spatial trajectory for a maneuvering aircraft in simulation tasks. The algorithm involves forming a sequence of motion segments that implement elementary maneuvers. Possible variants of the algorithm's construction, as well as limitations and extensions in its implementation, ar discussed.

The contact state between yarn and the disc in the disc friction false-twisting machine affects the yarn quality. Previous studies have the contact state between yarn and disc has been discussed. However, the specific imaging methods were not suggested. In this study, we developed an imaging system that uses a collaborative robot to accurately and instantly observe the contact state. Using this system, the apparent yarn thickness, twist angle, and inclination angle on all discs within the unit were measured precisely from three directions. As a result, the apparent yarn thickness was found to be influenced by the draw ratio. Additionally, as the yarn passed through discs, twist angle increased and inclination angle of yarn decreased, confirming their interrelationship. Furthermore, it was observed that twist angle decreased downstream of the disc unit, suggesting that the contact state between yarn and the disc becomes unstable in this region. It was revealed that this system could be utilized for evaluating various disc units in future studies.

The energy use pattern for unit operations of crop production varies under different agro climatic zones and across various farm categories. This study was conducted to determine the energy utilization pattern for cotton and maize crops for different farm size holdings under dryland conditions. Medium size farm holdings were observed as highest energy utilization for crop production i.e. 1723 MJ ha-1, for cotton and in case of maize 1673 MJ ha-1 and lowest utilization of energy was observed in small farm holdings i.e. 1142 MJ ha-1, 1213 MJ ha-1 for cotton and maize, respectively, due to more use of power sources and inputs. Among field operations, land preparation consumed maximum energy across all categories of farmers and fertilizer was observed as the dominant source of input-energy for cotton (55.1 to 64.1%) and maize (48.7 to 58.8%). Lowest machinery energy ratio and mechanization index values were observed in small farmers when compared with the medium and large farmers. From the study it was observed that crop productivity increases as the energy input increases and energy input increases with increase in farm size. The farmers in the selected clusters still depends on the traditional power sources and implements for majority of operations except few selected operations, which has less capacity to do operations.

Abstract Crop grading represents a critical component in enhancing agricultural production efficiency and optimizing supply chain management. Through systematic assessment of crop appearance, quality attributes, and dimensional characteristics, accurate determination of market compliance and evaluation of quality grades and commercial value can be achieved. This systematic review comprehensively analyzes 53 studies published between 2020 and 2025 that employed deep learning-based target detection methods for automated crop grading applications. We establish a taxonomic framework categorizing detection methodologies into three distinct paradigms: two-stage detectors (characterized by high precision but substantial computational overhead), single-stage detectors (offering optimal speed-accuracy trade-offs), and emerging Transformer-based approaches (demonstrating superior global modeling capabilities). Our analysis reveals significant algorithmic evolution trends, with the YOLO series exhibiting dominant application across more than 30 crop varieties, achieving high detection accuracy while maintaining real-time performance capabilities. We systematically identify key technical challenges: sparse feature extraction under complex agricultural environments, severe class imbalance in defect detection tasks, and deployment constraints in edge computing scenarios. We propose a comprehensive solution framework that integrates multimodal data fusion (RGB-D-NIR), lightweight architectural optimization strategies, and semi-supervised learning approaches capable of reducing annotation requirements by 60–80%. Performance benchmarking on public datasets demonstrates that target detection methods exhibit superior engineering applicability compared to segmentation approaches, with enhanced inference speed and improved robustness to environmental variations. For future research directions, we categorize priorities into four domains: intelligent annotation systems, environmental adaptability enhancement, model compression techniques, and cross-domain transfer learning. This review provides a comprehensive technical framework for deep learning-driven crop grading, offering actionable insights for researchers and practical deployment guidelines for precision agriculture applications. We anticipate this review will inspire broader adoption of deep learning target detection methodologies in agricultural domains, fostering technological advancement and innovation in this field.

Millets are typically sown by broadcasting and drilling methods by using traditional implements. This often results in non-uniform seed distribution, leading to irregular spacing and uneven number of plants. Additionally, line sowing, as compared with broadcasting, improves the efficiency of harvesting and threshing operations. In regions with small farm holdings, bullock drawn planter is a great alternative to take care of above-mentioned conditions. Thus, laboratory evaluation of bullock drawn planter was carried out using a stationary seed plate with agitator shaft as a metering mechanism. For millet seeds, a test rig was utilized to assess the effectiveness of seed metering in a laboratory condition at three distinct speed ratios (1.0, 1.5, and 2.0), cell sizes (3, 4, and 5 mm), and seed plate angle (0º, 15º, and 30º). The uniformity coefficient, multiple index, missing index, and quality of feed index were used as indicators to assess the planter's performance. At a speed ratio of 1.5, the uniformity coefficient was highest with a 5.0 mm cell size and a 0° seed plate angle. The missing index was higher at a seed plate angle of 30º and a cell size of 3.0 mm at a speed ratio of 2.0, whereas multiple index was higher at a 30º seed plate inclination angle and a 5 mm cell size at a speed ratio of 1.0. At a seed plate angle of 0º and a cell size of 5 mm, the highest quality feed index and seed rate were achieved, although the speed ratios were 1.5 and 1.0, respectively. Field evaluation of bullock-drawn millet planter, showed optimum speed ratio, cell size and seed plate angle of 1.5, 3 mm and 0º, respectively. It was found that the millet planter's effective field capacity and field efficiency were 0.24 ha h-1 and 85.7%, respectively. The bullock-drawn planter's break-even point and operating cost were determined to be 40.56 hours annually and Rs. 694.00 per hour, respectively. The developed planter enhances efficiency by reducing manual labour and ensuring more precise seed placement.

The electrical fields prevalent in the environment are significant enough to influence the natural pollination process. This study presents a comprehensive review of the concept of electric ecology in pollination, encompassing atmospheric electric fields, floral electric fields, the electrostatic charge accumulated on bees, electrostatic interactions between bees and flowers, the role of these interactions in pollen transfer, and artificial pollination techniques designed to mimic the behavior of pollinators. The potential difference between the ionosphere and the Earth's surface generates an Atmospheric Potential Gradient (APG); consequently, a positive charge in the atmosphere draws a negative charge to the Earth's surface as well as to the plants due to electrostatic induction charging. The charge disparity between the ground and the atmosphere creates an electric field around flowers, namely a floral electric field. Meanwhile, insect pollinators, especially bees, possess positive charges due to triboelectrification. The electrostatic forces of attraction between the bee and flower intensify rapidly, facilitating the transfer of pollen from the anther to the bee and vice versa. These electrostatic interactions contribute substantially to natural pollination processes. Understanding these phenomena is therefore crucial for the development of innovative, sustainable agricultural practices, especially for artificial pollination techniques by means of electrostatic pollinators.

Magnetically treated irrigation water (MIW) and potassium nano-fertilizers have emerged as innovative methods to improve plant physico-mechanical properties and promote sustainable agricultural practices. This study investigates the combined effects of MIW and varying potassium fertilization treatments on key physical and mechanical properties of sugar beet roots. A 2 × 5 split-plot design was employed, comparing MIW with non-magnetized irrigation water (NMIW) as a control, and five potassium fertilization regimes: 100% potassium oxide (K2O), 75% K2O + 25% nano-potassium (Nano K), 50% K2O + 50% Nano K, 25% K2O + 75% Nano K, and 100% Nano K. Physical properties, including root dimensions, and projected surface area (PSA), were analyzed using image processing. In addition, the volume was measured utilizing the water displacement technique. Mechanical properties, such as firmness and penetration energy, were measured to evaluate their role in promoting sustainable agricultural practices. Results from two growing seasons showed that the treatments combining MIW with 100% K2O and MIW with 75% K2O + 25% Nano K consistently achieved the highest values for root diameter, thickness, length, PSA, weight, firmness, and penetration energy. Notably, the MIW + 75% K2O + 25% Nano K treatment exhibited significant improvements in both physical and mechanical properties, contributing to enhanced resource efficiency and crop resilience. This approach highlights the potential of MIW and potassium nano-fertilizers to optimize sugar beet cultivation, offering a sustainable strategy to support future agriculture practices while minimizing environmental impact.

Cabbage (Brassica rapa L. ssp. Pekinensis) is an important vegetable crop in the Republic of Korea, due to its essential role in kimchi production. However, labor shortages and an aging population necessitate mechanization to sustain productivity. This study aimed to evaluate the field performance of a cabbage transplanter under development with a commercial transplanter and a cabbage collector under greenhouse conditions. This study evaluated transplanting efficiency, planting performance, and yield of cabbage using seedlings at three distinct age groups (30, 35, and 43 days). A cabbage transplanter (Transplanter A) under development, a commercial model (Transplanter B), and manual transplanting were used for comparative analysis. At harvest, a tractor-mounted cabbage collector was used to collect and pack all the cabbages. Transplanter A demonstrated a forward speed of 1.27 km/h and an average planting rate of 2365 seedlings/h, significantly higher than manual transplanting (513 seedlings/h). The effective field capacity (EFC) ranged from 0.11 to 0.13 ha/h, compared to 0.019–0.028 ha/h for manual planting. While Transplanter A showed a higher missing transplant rate (18.17%) than Transplanter B (7.67%), it maintained consistently lower bad planting rates (2.5–4.5%) compared to Transplanter B (3.3–8.8%). In addition, it produced significantly higher cabbage weights (6070 g/plant) and better root metrics than manual transplanting. The cabbage collector achieved 100% efficiency with no crop damage or contamination. The transplanter under development proved effective for greenhouse use, offering faster operation, better planting accuracy, and higher yields, supporting broader mechanization in Korean agriculture.

The results of numerous research studies published in recent years suggest that celecoxib (CEL) may be effective in the treatment of various skin disorders. However, to date, no semisolid product containing CEL has been launched. With a focus on the future development of topical products, we aimed to investigate the impact of different semisolid matrices on the in vitro performance of CEL. For this purpose, 1% (w/w) of the drug was suspended in 4 compounding vehicles available in Polish community pharmacies: Lekobaza (amphiphilic cream), Lekobaza Lux (hydrophobic cream), Celugel (hydrogel), and Oleogel (lipogel). Given their very different physicochemical properties, our goal was to analyze, for the first time, their influence on spreadability, viscoelastic properties and the release rate of CEL. It was found that all of the semisolid matrices were suitable as vehicles for the drug in terms of spreadability and rheological stability. The viscous properties predominated when Celugel was used as a vehicle, but when Lekobaza, Lekobaza Lux and Oleogel were tested, the elastic properties prevailed. The drug release rate was the highest when hydrophilic matrices, i.e., Celugel or Lekobaza were used, but when hydrophobic matrices such as Lekobaza Lux or Oleogel were examined, CEL was released slowly. These findings might be related not only to the properties of these matrices, but also to the design of the release study that was more suitable for evaluating the hydrophilic matrices. Celugel could be particularly useful as a vehicle for CEL for the therapy of large lesions with heavy exudation, but if there is a risk of skin drying out after using the hydrogel, the use of Lekobaza can be recommended.