AgriEngineering

To address the high energy consumption and low efficiency in shredding film–stalk mixtures during the resource utilization of cotton-field residues in Xinjiang—issues arising from the large mechanical-property differences among the mixture components—a custom single-support shearing fixture was developed to investigate the effects of residual-film wrapping layers, blade rake angle, sliding-cutting angle, and shearing speed on the F_j^max. Based on a Box–Behnken response surface design combined with analysis of variance and microscopic observations of the shearing process, the results showed that all main-effect factors had extremely significant influences on the F_j^max (p < 0.0001). Their relative contributions followed the following order: number of film wrapping layers > blade rake angle > shearing speed > sliding-cutting angle. Residual-film wrapping markedly increased shear resistance; increasing the sliding-cutting angle effectively reduced the shearing force; and reducing the rake angle facilitated more energy-efficient shredding. Interaction analysis further revealed significant coupling between sliding-cutting angle and shearing speed, rake angle and sliding-cutting angle, and rake angle and shearing speed (p < 0.05). Comparative shearing tests indicated that pure cotton stalks exhibited continuous brittle fracture with relatively stable force–displacement profiles, whereas film–stalk composites showed a sequentially coupled failure mode characterized by “residual-film pre-shearing–primary stalk fracture–secondary film stretching,” leading to multi-peak fluctuations in the force–displacement curves. Based on response surface optimization and mechanistic analysis, a parameter combination of a 35° rake angle, a 4–8° sliding-cutting angle, and medium-to-low shearing speed is recommended for shredding operations. This study elucidates the shearing and fragmentation mechanisms of film–stalk mixtures, provides theoretical guidance for optimizing key structural and operational parameters of post-recovery equipment, and offers important engineering value for promoting farmland residual-film pollution control and agricultural waste resource utilization. Full article

To implement a successful strawberry (Fragaria × ananassa) farming, fungal diseases must be detected in a timely manner so that informed crop protection decisions can be made. While field scouting is an option, it is manual and labor intensive. Scouting is also inaccurate and reduces efficiency due to micro-climatic lighting and field clutter, among other factors. StrawberryDualNet is a framework that supports Integrated Pest Management and automates symptom surveillance. We present dual-path CNN–Transformer fusion design that integrates two branches: a dynamic multi-scale convolution and a lightweight transformer. The former is able to capture fine-grained morphological lesion textures, while the latter captures overall contextual patterns. The two representations are fused through a learnable gating mechanism to decrease visual uncertainty amongst differing symptoms. We used a stratified five-fold cross-validation to evaluate the framework among five economically significant pathogens. Our approach significantly outperformed other automated scouting baselines, achieving 95.1% accuracy and 95.3% precision, respectively, and it is successful for Anthracnose, Gray Mold, Powdery Mildew, Rhizopus Rot, and Black Spot. The model is also scaled down compared to others (0.04 M parameters; 0.72 MB, 13–20× smaller than MobileNetV2/ShuffleNetV2) and is thus able to be deployed on devices that are lacking computational resources. For edge feasibility, we assessed reduced-precision inference; 16-bit floating point quantization preserved baseline performance at 83 FPS, whereas 8-bit integer quantization caused notable accuracy degradation. Overall, the proposed local–global fusion design provides an accurate, interpretable, and scalable tool for real-time disease phenotyping in precision horticulture. Full article

In the past two decades, important progress has allowed a better understanding of how light signals are perceived by plants, not only as a source of energy for photosynthesis but also as environmental cues that modulate growth, development, and stress responses. These advances open up promising prospects for light-based treatments in agriculture. This review synthesizes recent scientific findings on the application of specific wavelengths (from ultraviolet to infrared) to improve crop yield, quality, and resilience. The analysis focuses on controlled environment agriculture, where most experimental data have been generated and where the integration of lighting strategies is technically more feasible compared to open-field settings. Preharvest: we explore how spectral quality, intensity, and duration can be used to modulate plant growth, photosynthesis, defense pathways, and the accumulation of nutritional compounds. Postharvest: the focus shifts to how light can help maintain visual and nutritional quality, regulate ripening, limit pathogen development, and extend shelf-life. The review emphasizes plant photoreceptors and signal transduction pathways, as well as technical parameters such as spectrum selection, application timing, and lighting configuration. A selection of recent patents illustrates how fundamental research is being translated into deployable, energy-efficient lighting technologies for sustainable crop management. Full article

The harvesting of Camellia oleifera fruit in hilly areas faces core problems such as low manual efficiency, poor terrain adaptability of existing machinery, and severe emissions and noise from traditional equipment. This study designed a crawler chassis utilizing a permanent magnet synchronous motor-driven hydraulic system. The research integrated kinematic modeling and resistance calculations for parameter matching, followed by AMESim dynamic simulations and motor calibration experiments. Finally, comprehensive field tests were conducted to evaluate the prototype. The results indicate the chassis achieves a maximum travel speed >1.5 m∙s⁻¹, a climbing angle of 41.4°, and a turning radius of 0.72 m, with noise levels strictly below 80 dB(A). Significantly, dynamic power characteristic tests under actual vibration harvesting conditions revealed that the 45 kW motor maintains a rapid response with ample power reserve. The input power exhibited a distinct square-wave pattern synchronized with hydraulic valve commands, peaking at 18.1 kW during vibration bursts. These findings confirm the system’s stability under coupled driving and harvesting loads. This design offers a viable, low-noise solution for electrifying and intelligently upgrading Camellia oleifera harvesting equipment in complex terrains. Full article

This study investigates how cover crop management and soil tillage influence the development and yield of cucumber and cabbage crops. Three cover crop treatments—blue lupin, black oats, and their mixture—were evaluated during the autumn/winter season, while Stylosanthes capitata (Fabaceae), pearl millet (Pennisetum glaucum, Poaceae), and their mixture were assessed during the spring/summer season, under both conventional tillage and no-till (direct seeding) systems. Cover crops were established in spring/summer (October–November) and, after their management, cucumber (Cucumis sativus L.) was cultivated from December to February. Subsequently, winter cover crops were grown from May to July, followed by cabbage (Brassica oleracea var. capitata) cultivation from July to September. Drip irrigation was used, and organic practices were employed for weed, pest, and disease management. Germination, seedling survival rate, and plant growth (height, number of leaves, foliage cover, and fruit or cabbage size) were evaluated. Finally, crop yield is considered by comparing harvest weight and quality to determine which combination of soil cover and planting method maximizes crop productivity and quality. Obviously, management differences that influence yield will be associated with soil properties. To better understand the causes of these yield differences, the influence of soil chemical properties was explored using multivariate analysis techniques (discriminant analysis) and neural networks. Multivariate techniques allow for the exploration of complex relationships among multiple variables simultaneously, facilitating the identification of key patterns or factors that influence crop yield. On the other hand, neural networks, using machine learning models, allow for the prediction of outcomes based on the soil’s physicochemical properties, as well as the identification of optimal combinations of factors that maximize crop yield. Discriminant analysis and neural networks showed that soil variables such as pH, organic matter (OM), cation exchange capacity (CEC), phosphorus (P), and potassium (K) were key determinants in differentiating the yield groups. Cabbage yield was most strongly associated with pH and OM, while cucumber yield responded more strongly to potassium and CEC. Full article

Accurate wheat yield prediction is essential for ensuring food security and sustainable resource management under the increasing challenges of climate change. This study investigates the integration of unmanned aerial vehicle (UAV)-based multispectral imaging and machine learning (ML) techniques to improve yield forecasting in European wheat cultivars. Field experiments were conducted on 400 sub-plots with varying NPK fertilization regimes and five wheat varieties, monitored across six phenological stages during the 2023 growing season in Vojvodina, Serbia. A DJI Phantom 4 Multispectral UAV collected high-resolution imagery, from which 65 vegetation indices were computed. Using PyCaret’s automated ML framework, 25 regression algorithms were evaluated for yield prediction. Ensemble models, particularly Random Forest, Extra Trees, Gradient Boosting, and LightGBM, consistently outperformed linear and kernel-based approaches. The highest prediction accuracy was achieved with the Random Forest Regressor during full flowering (BBCH 65–69), yielding an R² of 0.952 and an RMSE of 0.44 t/ha. Results highlight the temporal dynamics of model performance, with optimal predictions occurring during reproductive stages. The findings confirm that UAV-derived multispectral data, coupled with ensemble machine learning, provide a non-invasive, accurate, and computationally efficient method for yield forecasting. This framework has significant potential for supporting precision agriculture, enabling real-time decision-making, and enhancing the resilience of wheat production systems. Full article

Apple leaf disease identification with high precision is one of the main challenges in precision agriculture. The datasets usually have class imbalance problems and environmental changes, which negatively impact deep learning approaches. In this paper, an ablation study is proposed to test three different scenarios: V1, a hybrid balanced dataset consisting of 10,028 images; V2, an imbalanced dataset as a baseline consisting of 14,582 original images; and V3, a 3× physical augmentation approach based on the 14,582 images. The classification performance of YOLOv11x was benchmarked against three state-of-the-art CNN architectures: ResNet-152, DenseNet-201, and EfficientNet-B1. The methodology incorporates controlled downsampling for dominant classes alongside scenario-based augmentation for minority classes, utilizing CLAHE-based texture enhancement, illumination simulation, and sensor noise generation. All the models were trained for up to 100 epochs under identical experimental conditions, with early stopping based on validation performance and an 80/20 train-validation split. The experimental results demonstrate that the impact of balancing strategies is model-dependent and does not universally improve performance. This highlights the importance of aligning data balancing strategies with architectural characteristics rather than applying uniform resampling approaches. YOLOv11x achieved its peak accuracy of 99.18% within the V3 configuration, marking a +0.62% improvement over the V2 baseline (99.01%). In contrast, EfficientNet-B1 reached its optimal performance in the V2 configuration (98.43%) without additional intervention. While all the models exhibited consistently high AUC values (≥99.94%), DenseNet-201 achieved the highest value (99.97%) across both V2 and V3 configurations. In fine-grained discrimination, the superior performance of YOLOv11x on challenging cases is verified, with only one incorrect classification (Rust to Scab), while ResNet-152 and DenseNet-201 incorrectly classified eight and seven samples, respectively. Degradation sensitivity analysis under controlled Gaussian noise and motion blur indicated that CNN baseline models maintained stable performance. High minority-class reliability, including a 96.20% F1-score for Grey Spot and 100% precision for Mosaic, further demonstrates effective fine-grained discrimination. Results indicate that data preservation with physically inspired augmentation (V3) is better than resampling-based balancing (V1), especially in terms of global accuracy and minority-class performance. Full article

Agrivoltaics (AVs), the co-location of photovoltaic panels and agricultural production, is increasingly promoted as a strategy to enhance land-use efficiency and support renewable energy transitions. While numerous studies emphasize potential synergies, growing evidence indicates that AV systems also entail significant biophysical, ecological and socio-economic trade-offs. This review synthesizes published literature on the negative impacts and management challenges associated with agrivoltaics across diverse crops, climates and institutional contexts. A structured literature analysis was conducted, integrating findings from experimental field studies, ecological assessments, economic evaluations and policy analyses. The reviewed evidence demonstrates that panel-induced shading and altered microclimatic conditions frequently reduce photosynthetically active radiation, modify soil temperature and moisture regimes, and impair photosynthetic efficiency, yield stability, and quality in light-demanding crops. Open-field AV installations further alter understory vegetation, pollinator activity and soil arthropod communities, leading to functional biodiversity losses beneath panel-covered areas. Economic and institutional analyses reveal high investment costs, regulatory ambiguity and land-tenure constraints that disproportionately transfer agronomic and financial risks to farmers, while land-use conflicts may reduce food production and contribute to indirect land-use change. Overall, open-field AV outcomes are strongly context- and design-dependent. The review highlights the need for long-term, integrative assessments and governance frameworks that explicitly address trade-offs to ensure that AVs contribute to sustainable land-use transitions rather than undermining agricultural and ecological functions. Full article

The feather coverage of a laying hen is an important indicator of both its productivity and welfare. Conventional manual feather scoring procedures are laborious, subjective, and stressful for the hens. Thermography offers a modern alternative to addressing these problems. Thermal cameras capture radiative heat loss, which is comparatively greater Classification from featherless areas. Studies have been conducted to establish a standard temperature range that correlates to specific featherless areas. However, such temperature-based approaches have been inconsistent with each other. In contrast, this study used deep learning techniques to automatically assess dorsal feather scores using thermal images. Thermal images (n = 1575) of the dorsal body of cage-free laying hens with varying degrees of feather damage were captured. Manual feather scoring was performed, classifying the image into a feather score (0–2) according to the increasing severity of feather loss. A total of 1222 images were selected, filtering out images of lower quality. Two types of computer vision models, a classification model and an object detection model, were trained and evaluated. A custom convolutional neural network (CNN) was trained to classify thermal images into feather score categories. Additionally, we trained and optimized You Only Look Once (YOLO) object detection models to detect areas of feather damage and predict the feather score. The CNN model achieved an overall accuracy of 0.81, with high precision for severe feather loss. The YOLO-based object detection model was optimum using YOLO11n, which achieved a precision of 0.81, a recall of 0.73 and a mean average precision (mAP) at 0.5 intersection over union (IoU) of 0.84. Results show the potential of combining thermal imaging with deep learning techniques to perform objective, automatic, and scalable feather scoring procedures. Future studies should focus on data diversity, multiple part scoring, and semantic segmentation for robust performance. Full article

Accurate and in-situ monitoring of frost growth on plant leaves is crucial for disaster prevention in smart agriculture. To address the limitations of traditional methods in quantification and continuity, this study proposes a novel monitoring paradigm integrating dynamic dielectric spectrum analysis with hybrid intelligent algorithms. A mesh-electrode-based capacitive sensor was designed to capture in-situ and continuous dielectric spectrum changes on leaf surfaces. Subsequently, a hybrid SWT-SSA-LSTM model was constructed for high-fidelity denoising and prediction of the original signals. Field experiments demonstrated that this system could quantify frost layer mass and thickness with high precision. The established nonlinear regression models achieved coefficients of determination of 0.924 and 0.975, respectively. The prediction model exhibited outstanding performance, with a root mean square error as low as 1.475. This study establishes a complete technical closed-loop from physical perception to intelligent prediction, providing an innovative solution for precise frost monitoring in agriculture. Full article

Arable land quality is of the essence for the sustenance of grain production and food security. The continuous monitoring of the physical and chemical properties of arable land is instrumental in facilitating a comprehensive understanding of the evolution patterns of soil quality. This, in turn, provides fundamental evidence that is crucial for the optimization of cultivation practices, the establishment of appropriate plough layers, and the enhancement of soil quality. The near-surface sensing methodologies facilitate the acquisition of soil data at reduced scales, thus signifying a pivotal research trajectory for the procurement of soil-related information. The present study undertakes an examination of the current state of research on acquiring key parameters of farmland soil and provides an overview of the fundamental ground-level techniques employed for the assessment of farmland soil parameters. These techniques encompass single-parameter fixed-point detection, encompassing Soil Moisture Content (SMC), Soil Electrical Conductivity (EC), and nutrient analysis, multi-parameter fusion detection, and dynamic parameter monitoring. The study systematically reviews field sensing methods for major soil physicochemical parameters (such as SMC, Soil Penetration Resistance (SPR), EC, and nutrients) while analyzing the current application of Artificial Intelligence (AI) in soil parameter detection. The present paper proposes a developmental trajectory that shifts from “single-parameter static” to “multi-parameter dynamic” monitoring. This trajectory is proposed as a building upon the analysis of existing research. This evolution emphasizes intelligent algorithm-driven data enhancement to improve detection accuracy, forming a closed-loop progression of “dynamic detection—precise modeling—decision support”. This framework provides a reference for the advancement of soil sensing monitoring technologies and the scaling of precision agriculture applications. Full article

The cultivation of potatoes is essential for rural food security, and the use of Unmanned Aerial Vehicle Red-Green-Blue (UAV-RGB) imagery allows for precise and cost-effective estimation of yield and identification of varieties, overcoming the limitations of manual assessment. We evaluated four INIA varieties (Bicentenario, Canchán, Shulay and Tahuaqueña) by integrating agronomic measurements (height, number and weight of tubers, leaf health) with color and textural indices derived from RGB orthomosaics. Yield prediction was modeled using Random Forest (RF) and Gradient Boosting (GB); varietal identification was approached with (i) a Convolutional Neural Network (CNN) that classifies RGB images and (ii) classical models such as Random Forest, Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), Decision Trees and Logistic Regression trained on EfficientNetB0 embeddings. The results showed significant genotypic differences in yield (p < 0.001): Tahuaqueña 13.86 ± 0.27 t ha⁻¹ and Bicentenario 6.65 ± 0.27 t ha⁻¹. The number of tubers (r = 0.52) and plant height (r = 0.23) correlated with yield; RGB indices showed low correlations (r < 0.3) and high redundancy (r > 0.9). RF achieved a better fit (Coefficient of determination, R² = 0.54; Root Mean Square Error, RMSE = 2.72 t ha⁻¹), excelling in stolon development (R² = 0.66) and losing precision in maturation due to foliar senescence. In classification, the CNN and RF on embeddings achieved F1-macro ≈ 0.69 and 0.66 (Receiver Operating Characteristic—Area Under the Curve, ROC AUC RF = 0.89), with better identification of Bicentenario and Shulay. We conclude that UAV-RGB is a cost-effective alternative for phenotypic monitoring and varietal selection in high Andean contexts. These findings support the integration of UAV-RGB imagery into breeding and monitoring pipelines in resource-limited Andean systems. Full article

Agricultural plastic waste (APW) is an emerging source of soil pollution and potential micro- and nano-plastic (MNP) contamination in agroecosystems. This study focuses on the Apulia region in southern Italy, a key horticultural and viticultural area with intensive plastic use. Annual APW was estimated for each agricultural feature using a detailed 1:5000 land use map, crop distribution data, and validated plastic waste indices for several plastic application types. The analysis was integrated within a Geographic Information System (GIS) and combined with relative risk indices (RRIs) to compute and map the agricultural plastic pollution risk index (APPRI), a semi-quantitative indicator designated to estimate the potential release of MNPs from agricultural plastics. The APPRI is obtained by multiplying the APW estimates by the RRIs. The results show a clear spatial heterogeneity in plastic waste generation, with the highest APPRI values in vineyards, orchards, olive groves, and greenhouse systems, particularly in the provinces of Foggia and Bari. Cereal-based cropping systems exhibited the lowest risk values. The study proposes an innovative approach, combining land use, APW, and related potential risk into a single mapping tool. This allows for effectively identifying regional hotspots where management and recycling strategies should be prioritized. This GIS-based tool for assessing and visualizing agricultural plastic pollution risk can support evidence-based decision-making and sustainable waste management in agricultural landscapes. Full article

Accurate forecasting of crop maturity supports efficient harvest planning and accelerates selection decisions in breeding programs. In spring wheat, maturity is typically assessed through manual scoring late in the season, which limits its usefulness for timely harvest management and early selection decisions in breeding programs. This study evaluated uncrewed aerial vehicle (UAV)–based multispectral imagery for forecasting maturity in spring wheat grown at Lethbridge, Alberta (AB), Canada, during the 2024 and 2025 growing seasons. Thirty cultivars were monitored using seven-band UAV multispectral imagery during grain filling, enabling derivation of core vegetation and senescence-related indices from radiometrically calibrated orthomosaics. Strong correlations ($\left|r\right|>0.85$) were observed between vegetation indices and days remaining to maturity (DRTM), motivating baseline regression models and subsequent evaluation of eleven machine-learning and deep-learning approaches. Among these, support vector regression (SVR) and multi-layer perceptron (MLP) achieved the highest predictive accuracy ($R^2=0.95$–$0.96$; mean absolute error (MAE) $\approx 1.25$ days). Deep learning models achieved performance comparable to machine-learning approaches; however, incorporating spatial information through convolutional neural networks did not improve prediction accuracy. Feature-attribution analysis identified the red, red-edge (RE), and near-infrared (NIR) spectral bands as key predictors, enabling non-destructive, early, and scalable UAV-based maturity forecasting. Full article

This study addresses the challenge of instance segmentation for key grapevine structures (Trunk, Branch, and Bud) in complex natural environments, focusing on issues such as varying light conditions, weather, and significant scale variations. We propose an enhanced instance segmentation model named CDA-YOLOv8. Trained on a self-built dataset of 2160 images covering grapevine scenes under diverse lighting conditions, this model integrates three key components: the ACmix module for enhanced global feature modeling, the C2f-DWR module for optimized multi-scale feature extraction, and the CSPPC module for achieving model lightweighting. We evaluate performance using precision/recall and mAP@50, together with the stricter mAP@[50:95], for segmentation quality, and parameters/model size/FPS for deployment efficiency. Experimental results demonstrate that CDA-YOLOv8 achieves 70.1% precision, 74.4% recall, 76.3% mAP@50, and 36.8% mAP@[50:95], with only 3.19 million parameters and a compact model size of 6.49 MB. Compared with the original YOLOv8-seg, CDA-YOLOv8 improves segmentation accuracy while maintaining high efficiency (6.87 FPS). It also delivers better mask quality under stricter overlap criteria, providing quantitative evidence for real-time perception in automated grapevine pruning systems. Full article

This study presents the design and evaluation of a low-cost, manually operated hydraulic press for extracting oil from Kalahari melon seeds in rural Namibia. Traditional firewood-based extraction methods are labor-intensive, inefficient, and pose health and environmental risks. Guided by contextual engineering principles, the manual hydraulic press was co-designed with local women and fabricated from low-cost, readily available scrap materials sourced from rural markets. Experimental trials were conducted in triplicate (n = 3 per treatment) to evaluate four seed pre-treatment conditions, (1) roasted and crushed, (2) unroasted and crushed, (3) roasted and uncrushed, and (4) untreated, and two pressing chamber diameters (5.08 cm and 7.63 cm). The manual hydraulic press reduced processing time from approximately 5–8 h to less than 5 min per batch and increased oil yield from reported traditional values of 5–7% to 8.5–9.5% by mass. Participants reported improved oil clarity, reduced physical strain, and enhanced usability during field testing. While long-term scalability and social or environmental impacts were not directly assessed, the results demonstrate community-scale technical performance and short-term user acceptance of a firewood-free oil extraction technology. Findings are discussed in relation to gendered labor demands and clean-energy processing in off-grid contexts, without implying direct measurement of Sustainable Development Goal outcomes. Full article

Whole-plant maize (corn) (WPC) is a critical forage in ruminant diets, and rapid, reliable measurement of its nutritional composition is essential for precision feeding. We hypothesized that an on-site near-infrared spectroscopy (OS-NIRS—specifically, HarvestLab™ 3000) sensor would provide within-laboratory repeatability comparable to commercial analytical laboratories (ALs) and inter-laboratory reproducibility similar to conventional laboratory analyses. To test this, WPC samples were collected across three experiments and two countries (USA and Germany) and analyzed by both OS-NIRS and ALs, with precision metrics calculated according to ISO 5725. Results showed that OS-NIRS achieved intra-laboratory repeatability equal to or greater than ALs, particularly for protein and starch. The repeatability performance of the OS-NIRS sensors was similar to that of ALs for moisture and NDF. Inter-laboratory reproducibility varied widely across constituents and experiments. Including OS-NIRS data with AL measurements produced inconsistent effects—sometimes narrowing confidence intervals but more often widening them—while OS-NIRS data alone demonstrated repeatability on par with ALs but mixed reproducibility outcomes. Inclusion of OS-NIRS data did not introduce systematic bias and, in some cases, improved consistency. These findings indicate that OS-NIRS can complement laboratory analyses by providing timely, farm-level measurements that enhance decision-making in feed management. Full article

The dairy sheep, vital to the Mediterranean economy, struggles to balance productivity, sustainability, and animal welfare, especially in extensive, small-scale systems. Precision livestock farming (PLF) technologies offer new opportunities by enabling continuous, non-invasive, and data-driven monitoring across diverse farming conditions. Despite rapid progress in sensors, computer vision, wearable devices, and artificial intelligence (AI), a comprehensive synthesis focused on dairy sheep remains limited. This review provides an updated overview of PLF applications in dairy sheep farming, based on a literature review. The 2018–2025 timeframe was chosen to capture recent advances in Internet of Things (IoT), AI, and sensor technologies that have achieved practical relevance only in recent years. The review identifies core technological domains such as automated weight and body condition monitoring, biometric identification, wearable and IoT-based sensors, localization systems, behavioral and thermal monitoring, virtual fencing, drone-assisted herding, and advanced decision-support tools. Innovations including lightweight deep-learning models, multimodal sensing frameworks, and digital twins highlight the growing potential for scalable, real-time applications. While technological progress is substantial, practical adoption is hindered by economic, technical, interoperability, and ethical barriers. This review consolidates current evidence and identifies future priorities to guide the development of integrated, welfare-focused PLF solutions for dairy sheep farming. Full article