Strawberry post-harvest losses are estimated at 50%, due to improper handling and harvest timing, necessitating the use of non-invasive methods. This study develops a non-invasive in situ bioelectrical spectroscopy for strawberry peduncles. Based on traditional assessments and invasive metrics, 100 physiologically ripe (PR) and 100 commercially mature (CM) strawberries were distinguished. Spectra from their peduncles were measured from 1 kHz to 1 MHz, collecting four parameters (magnitude (Z(f)), phase angle (θ(f)), resistance (R(f)), and reactance (X(f))), resulting in 80,000 raw data points. Through systematic spectral preprocessing, Bode and Cole–Cole plots revealed a distinction between PR and CM strawberries. Frequency selection identified seven key frequencies (1, 5, 50, 75, 100, 250, 500 kHz) for deriving 37 engineered features from spectral, extrema, and derivative parameters. Feature selection reduced these to 6 parameters: phase angle at 50 kHz (θ (50 kHz)); relaxation time (τ); impedance ratio (|Z1k/Z250k|); dispersion coefficient (α); membrane capacitance (Cm); and intracellular resistivity (ρi). Four algorithms (TabPFN, CatBoost, GPC, EBM) were evaluated with Monte Carlo cross-validation with five iterations, ensuring robust evaluation. CatBoost achieved the highest accuracy at 93.3% ± 2.4%. Invasive reference metrics showed strong correlations with bioelectrical parameters (r = 0.74 for firmness, r = −0.71 for soluble solids). These results demonstrate a solution for precise harvest classification, reducing post-harvest losses without compromising marketability.

Greenhouses are crucial for maintaining an ideal temperature and humidity level for plant growth; however, attaining ideal levels remains a challenge. Energy-efficient and sustainable alternatives are needed because traditional temperature/humidity control practices and vapor compression air conditioning systems depend on climate conditions and harmful refrigerants. Advanced alternative technologies like evaporative cooling and desiccant dehumidification have emerged that maintain the ideal greenhouse temperature and humidity while using the least amount of energy. This study reviews direct evaporative cooling, indirect evaporative cooling, and Maisotsenko-cycle evaporative cooling (MEC) systems and solid and liquid desiccant dehumidification systems. In addition, integrated desiccant and evaporative cooling systems and hybrid systems are reviewed in this study. The results show that the MEC system effectively reduces the ambient temperature up to the ideal range while maintaining the humidity ratio, and both dehumidification systems effectively reduce the humidity level and improve evaporative cooling efficiency. The integrated systems and hybrid systems have the ability to increase energy efficiency and controlled climatic stability in greenhouses. Regular maintenance, initial system cost, economic feasibility, and system scalability are significant challenges to implement these advanced temperature and humidity control systems for greenhouses. These findings will assist agricultural practitioners, engineers, and researchers in seeking alternate efficient cooling methods for greenhouse applications. Future research directions are suggested to manufacture high-efficiency, low-energy consumption, and efficient greenhouse temperature control systems while considering the present challenges.

The use of artificial intelligence tools in agriculture is growing. In particular, the use of ensemble models. However, there are still few reviews on the use of these models in the study of the impacts of climate change on agriculture. Therefore, the aim of this article is to review the use of such models and perform three key tasks: (1) identify topics in which ensemble models are used, (2) determine the most widely applied model and the predominant crops and regions, and (3) explore future applications and challenges. As a result, it was noted that the first studies, dating back to 2011, applied ensemble models to model invasive species. Since then, research has focused on changes in temperature and precipitation, with at least one study published every year. The most cited studies have dealt with land use classification, emphasizing its relevance to climate change studies. Notably, studies on carbon storage in soil and its capacity to remove CO2 from the atmosphere have become increasingly relevant. This analysis highlights the growing importance of ensemble models in climate-related agricultural research, outlining trends and key areas for future exploration.

The adsorption performance of maize seeds in air-suction seed metering devices directly affects the operational quality of maize seeders. The suction holes on the seed metering disc play a crucial role in determining the device’s ability to adsorb maize seeds and serve as a key design parameter for air-suction seed metering systems. Existing research has primarily focused on seed posture control and suction force models for standard particles, while experimental studies on the actual adsorption performance of maize seeds remain scarce. To further investigate the adsorption characteristics of maize seeds under different suction hole geometries, this study employed a self-developed adsorption force measurement platform to conduct experiments on maize seeds in various adsorption postures. The resulting force–displacement curves reveal the variation of adsorption force as seeds detach from the suction holes. To assess the applicability of conventional suction force calculation models, computational fluid dynamics (CFD) simulations were performed to analyze the adsorption mechanism of standard particles. The simulation results indicate significant limitations in commonly used suction force estimation methods. For instance, in experiments evaluating the effect of equivalent adsorption area, the relative error between the suction force estimated by the traditional pressure-based method for triangular holes and the actual measured force reached 40.82%. Similarly, the relative error between the force estimated by the airflow drag method for square suction holes and the actual measured force under the same conditions was 17.14%. Therefore, when evaluating actual seed adsorption, it is essential to comprehensively consider factors such as suction hole geometry, blocked suction area, seed shape, vacuum pressure, and the overlap depth between the seed boundary and the suction hole, all of which significantly influence the adsorption effect.

This study presents a bibliometric review aimed at mapping and analyzing the scientific literature related to the ethical implications of artificial intelligence (AI) in livestock farming, which is a rapidly emerging yet still underexplored field in international research. Based on the Scopus database, 151 documents published between 2015 and 2025 were identified and analyzed using the VOSviewer version 1.6.20 and Biblioshiny for Bibliometrix (RStudio version 2023.12.1) tools. The results show a significant increase in publications from 2021 onwards, reflecting the growing maturity of discussions around the integration of digital technologies in the agricultural sector. Keyword co-occurrence and bibliographic coupling analyses revealed the formation of four main thematic clusters, covering technical applications in precision livestock farming as well as reflections on governance, animal welfare, and algorithmic justice. The most influential authors, high-impact journals, and leading countries in the field were also identified. As a key contribution, this study highlights the lack of robust ethical guidelines and proposes future research directions for the development of regulatory frameworks, codes of conduct, and interdisciplinary approaches. The findings underscore the importance of aligning technological innovation with ethical responsibility and social inclusion in the transition to digital livestock farming.

In the bioenergy industry, highway hauling cost is typically 30%, or more, of the average cost of feedstock delivered to a biorefinery. Thus, truck productivity, in terms of Mg/d/truck, is a key issue in the design of a logistics system. One possible solution to this problem that is being explored is the utilization of modular pellet depots. In such a logistics system, raw biomass (i.e., low-bulk-density product) is converted into pellets (i.e., high-bulk-density product) by several smaller-scale modular pellet depots instead of by a single larger-capacity pellet depot. A truckload of raw biomass (e.g., round bales) is 16 Mg and a load of pellets is 34 Mg. The distribution of depots across a feedstock production area can potentially have an impact on the total truck operating hours (i.e., raw biomass hauling to a depot + pellet hauling from the depot to the biorefinery) required to deliver feedstock for annual operation of a biorefinery. This study examined three different distributions of depots across five feedstock production areas. The numbers of depots were one, two, and four per production area for totals of five, ten, and twenty depots. Increasing the number of depots from five to ten reduced raw biomass hauling hours by 12%, and increasing from five to twenty reduced these hours by 30%. Total hauling hours (raw biomass + pellets) were reduced by less than 1% with an increase from five to ten and by about 11% with an increase from five to twenty. The modular pellet depot concept demonstrated potential for providing improvements to biorefinery logistics systems, but more research is needed to optimize this balance.

Reusing agro-industrial by-products is a successful strategy that aligns with the 2030 Sustainable Development Goals. Red grape pomace poses a significant environmental challenge, particularly for wine-producing nations. Due to its high moisture content and seasonal availability, ensiling emerges as a potential method to prolong the nutritional value of red grape pomace, supporting the need for research into its application in animal nutrition. This study analyzed the bale ensiling process for red grape pomace by assessing its potential integration into ruminant diets and comparing its storage stability to untreated preservation methods. Baled silage units (approximately 300 kg each) were employed for this purpose. Analytical evaluations were conducted at 0, 7, 14, 35, 60, and 180 days of storage to monitor microbial and fermentation activity, nutritional composition, and bioactive attributes. Bale silage preserved the nutritional and microbial quality of red grape pomace for ruminant feed over a storage period of 180 days. The results demonstrated that bale silage successfully maintained the macro-composition, bioactive compounds, and antioxidant properties while reducing the fatty acid profile’s susceptibility to oxidation. By contrast, untreated storage led to significant spoilage. We concluded that bale ensiling is a suitable and effective technique that preserves red grapes for ruminant feed over a long period.

This study evaluates the impact of grape variety and harvesting method—manual versus mechanized—on must composition, wine quality, and economic performance in the Târnave viticultural area of Romania. Four grape varieties—Pinot Noir, Sauvignon Blanc, Fetească Regală, and Muscat Ottonel—were analyzed. Manual harvesting increased reducing sugars by 4.3–5.1 g/L and decreased titratable acidity by 0.6–0.8 g/L, particularly in Pinot Noir and Muscat Ottonel. Alcohol content was higher by 0.4–0.6 vol% in manually harvested samples, and dry extract increased by 1.0–1.3 g/L. Mechanized harvesting raised catechin concentrations by 15–19 mg/L due to enhanced skin maceration, but also slightly elevated volatile acidity (by ~0.1 g/L). From an economic perspective, labor cost was reduced from 480 lei/ton (approx. EUR 96) for manual harvesting to 120 lei/ton (approx. EUR 24) with mechanization. Fuel and maintenance costs for mechanized equipment averaged 85 lei/ha (EUR 17), and equipment depreciation was estimated at 100 lei/ton (EUR 20). The total harvesting cost per ton decreased from 480–520 lei to 300–320 lei (approx. EUR 96 to EUR 64), representing a ~38% reduction. The study supports a hybrid approach: manual harvesting for sensitive or premium cultivars, and mechanization for cost-efficient, large-scale production, aligning wine quality goals with economic sustainability.

The main objective of this study was to evaluate the possibilities of visible-near-infrared spectroscopy for investigating water and nitrogen stress in tomato plants. Two varieties of tomato plants (Red Bounty and Manusa) were grown in a greenhouse. Plants were divided into three groups: control, reduced nitrogen fertilization, and reduced watering. Spectral measurements of tomato leaves were made on-site. A USB4000 spectrometer for 450–1100 nm and a handheld AlbaNIR for the 900–1650 nm region were used for the spectra acquisition. Twenty-four vegetative indices were calculated using the reflectance characteristics of plants. Soft Independent Modeling of Class Analogy (SIMCA) models were developed for classification. Additionally, aquagrams were calculated. Results show differences between the spectra of leaves from control and stressed plants for both tomato varieties. Aquagrams clearly show the differences in water structures in the three groups of plants. The performance of developed SIMCA models for discriminating plants according to growing conditions was very high. The total accuracy was between 86.89% and 97.09%. Several vegetation indices successfully differentiate control and stressed plants for both tomato varieties. The results show successful differentiation of the control and stressed tomato plants based on spectral characteristics of the plants’ leaves in the visible and near-infrared region.

This study presents the development of a system based on convolutional neural networks for the automated detection and counting of Gossypium barbadense fruits, specifically the IPA cotton variety, during its maturation stage, known as “mota”, in crops located in the Lambayeque region of northern Peru. To achieve this, a dataset was created using images captured with a mobile device. After applying data augmentation techniques, the dataset consisted of 2186 images with 70,348 labeled fruits. Five deep learning models were trained: two variants of YOLO version 8 (nano and extra-large), two of YOLO version 11, and one based on the Faster R-CNN architecture. The dataset was split into 70% for training, 15% for validation, and 15% for testing, and all models were trained over 100 epochs with a batch size of 8. The extra-large YOLO models achieved the highest performance, with precision scores of 99.81% and 99.78%, respectively, and strong recall and F1-score values. In contrast, the nano models and Faster R-CNN showed slightly lower effectiveness. Additionally, the best-performing model was integrated into a web application developed in Python, enabling automated fruit counting from field images. The YOLO architecture emerged as an efficient and robust alternative for the automated detection of cotton fruits and stood out for its capability to process images in real time with high precision. Furthermore, its implementation in crop monitoring facilitates production estimation and decision-making in precision agriculture.