Search Results (3,812)

Accurate and timely estimation of soybean emergence at the plot scale using unmanned aerial vehicle (UAV) remote sensing imagery is essential for germplasm evaluation in breeding programs, where breeders prioritize overall plot-scale emergence rates over subimage-based counts. This study proposes PlotCounter, a deep learning regression model based on the TasselNetV2++ architecture, designed for plot-scale soybean seedling counting. It employs a patch-based training strategy combined with full-plot validation to achieve reliable performance with limited breeding plot data. To incorporate additional agronomic information, PlotCounter is extended into a multitask learning framework (MTL-PlotCounter) that integrates sowing metadata such as variety, number of seeds per hole, and sowing density as auxiliary classification tasks. RGB images of 54 breeding plots were captured in 2023 using a DJI Mavic 2 Pro UAV and processed into an orthomosaic for model development and evaluation, showing effective performance. PlotCounter achieves a root mean square error (RMSE) of 6.98 and a relative RMSE (rRMSE) of 6.93%. The variety-integrated MTL-PlotCounter, V-MTL-PlotCounter, performs the best, with relative reductions of 8.74% in RMSE and 3.03% in rRMSE compared to PlotCounter, and outperforms representative YOLO-based models. Additionally, both PlotCounter and V-MTL-PlotCounter are deployed on a web-based platform, enabling users to upload images via an interactive interface, automatically count seedlings, and analyze plot-scale emergence, powered by a multimodal large language model. This study highlights the potential of integrating UAV remote sensing, agronomic metadata, specialized deep learning models, and multimodal large language models for advanced crop monitoring. Full article

The extensive use of conventional pesticides has been a fundamental strategy in modern agriculture for controlling pests and increasing crop productivity; however, their improper application poses significant risks to human health and environmental sustainability. This review compiles scientific evidence linking pesticide exposure to oxidative stress and genotoxic damage, particularly affecting rural populations and commonly consumed foods, even at levels exceeding the maximum permissible limits in fruits, vegetables, and animal products. Additionally, excessive pesticide use has been shown to alter soil microbiota, negatively compromising long-term agricultural fertility. In response to these challenges, recent advances in nanotechnology offer promising alternatives. This review highlights the development of nanopesticides designed for controlled release, improved stability, and targeted delivery of active ingredients, thereby reducing environmental contamination and increasing efficacy. Moreover, emerging nanobiosensor technologies, such as e-nose and e-tongue systems, have shown potential for real-time monitoring of pesticide residues and soil health. Although pesticides are still necessary, it is crucial to implement stricter laws and promote sustainable solutions that ensure safe and responsible agricultural practices. The need for evidence-based public policy is emphasized to regulate pesticide use and protect both human health and agricultural resources. Full article

This paper introduces a robust and imperceptible video watermarking framework designed for blind extraction in dynamic video environments. The proposed method operates in the spatial domain and combines multiscale perceptual analysis, adaptive Just Noticeable Difference (JND)-based quantization, and temporal redundancy via multiframe embedding. Watermark bits are embedded selectively in blocks with high perceptual masking using a QIM strategy, and the corresponding DCT coefficients are estimated directly from the spatial domain to reduce complexity. To enhance resilience, each bit is redundantly inserted across multiple keyframes selected based on scene transitions. Extensive simulations over 21 benchmark videos (CIF, 4CIF, HD) validate that the method achieves superior performance in robustness and perceptual quality, with an average Bit Error Rate (BER) of 1.03%, PSNR of 50.1 dB, SSIM of 0.996, and VMAF of 97.3 under compression, noise, cropping, and temporal desynchronization. The system outperforms several recent state-of-the-art techniques in both quality and speed, requiring no access to the original video during extraction. These results confirm the method’s viability for practical applications such as copyright protection and secure video streaming. Full article

Amaranthus is appointed as a common weed associated with crops. The research was designed to survey the Amaranth existence pattern throughout the Fayoum Depression, Egypt, accompanied with a community vegetation analysis. The study was extended to collect and analyze associated soil samples. The obtained results figured out the prevalence of dicot families, herb growth forms, therophyte followed by phanerophyte life forms, the Pantropical monoregional chorotype, and the Mediterranean and Sudano-Zambezian followed by the Irano-Turanian pluri-regional chorotype. Multilevel pattern analysis stated that Gossypium barbadense, Corchorus olitorius, Sorghum bicolor, Sesamum indicum, and Zea mays are indicator species most related to Amaranth occurrence and prediction. NMDS analysis denoting that the Ibshaway, Youssef Al Seddik, Itsa, and Fayoum districts are the most representative districts for Amaranth existence on the basis of edaphic resources. Itsa and Youssef Al Seddik, in addition to Itsa and Fayoum, resemble each other in species composition. High pH and CaCO₃ percentages were discriminatory in Ibshaway, Itsa, and Youssef Al Seddik. Ni was the cornerstone for districts partitioning in pruned trees. Finally, Amaranth was flourishing in both comfortable and harsh habitats with cultivated crops and orchards, as well as on the outskirts. The findings are considered to be valorized by decision makers in arable land management. Full article

The objective of this study was to evaluate the physical-hydric properties of a Planosol under an Integrated Crop–Livestock–Forest (ICLF) system in the Agreste region of Paraíba, Brazil, after eight years of implementation, and to compare them with areas under a conventional cropping system and secondary native vegetation. The experiment was conducted at the experimental station located in Alagoinha, in the Agreste mesoregion of the State of Paraíba, Brazil. The experimental design adopted was a randomized block design (RBD) with five treatments and four replications (5 × 4 + 2). The treatments consisted of: (1) Gliricidia (Gliricidia sepium (Jacq.) Steud) + Signal grass (Urochloa decumbens) (GL+SG); (2) Sabiá (Mimosa caesalpiniaefolia Benth) + Signal grass (SB+SG); (3) Purple Ipê (Handroanthus avellanedae (Lorentz ex Griseb.) Mattos) + SG (I+SG); (4) annual crop + SG (C+SG); and (5) Signal grass (SG). Two additional treatments were included for statistical comparison: a conventional cropping system (CC) and a secondary native vegetation area (NV), both located near the experimental site. The CC treatment showed the lowest bulk density (1.23 g cm⁻³) and the lowest degree of compaction (66.3%) among the evaluated treatments, as well as a total porosity (TP) higher than 75% (0.75 m³ m⁻³). In the soil under the integration system, the lowest bulk density (1.38 g cm⁻³) and the highest total porosity (0.48 m³ m⁻³) were observed in the SG treatment at the 0.0–0.10 m depth. High S-index values (>0.035) and a low relative field capacity (RFc < 0.50) and K_θ indicate high structural quality and low soil water storage capacity. It was concluded that the SG, I+SG, SB+SG, and CC treatments presented the highest values of soil bulk and degree of compaction in the layers below 0.10 m. The I+SG and C+SG treatments showed the lowest hydraulic conductivities and macroaggregation. The SG and C+SG treatments had the lowest available water content and available water capacity across the three analyzed soil layers. Full article

Enhancing the nutritional content of crops is crucial for safeguarding human health and mitigating global hunger. A viable method for attaining this goal is the planned implementation of various agronomic practices, including tillage and nutrient provision. A field experiment was executed at the Hungarian University of Agriculture and Life Sciences in Gödöllő in the 2023 and 2024 growing seasons. The study aimed to assess the effects of foliar nutrient supply and soil tillage methods on the grain nutritional composition and mineral content of winter barley. Employing a split-plot design with three replications, the experiment included four nutrient treatments (control, bio-cereal, bio-algae, and MgSMnZn blend) and two soil tillage types (i.e., plowing and cultivator). The results indicated that while protein content was not influenced by the main effects of nutrients and tillage, the levels of β-glucan, starch, crude ash, and moisture content were significantly (p < 0.05) affected by the nutrient treatments and by growing year, treated as a random factor. Notably, bio-algae and bio-cereal nutrients, combined with cultivator tillage, enhanced β-glucan content. All applied nutrient treatments increased the level of starch compared to the control. With regard to grain mineral content, the iron and zinc content responded to the nutrient supply, tillage, and growing year. However, applying a multiple-nutrient composition-based treatment did not increase iron and zinc levels, suggesting that individual applications may be more effective for increasing the content of these minerals in grains. Cultivator tillage improved iron and zinc levels. Moreover, manganese (Mn) and copper (Cu) were predominantly affected by nutrient availability and by growing seasons as a random factor. Therefore, to improve grain quality, this study emphasizes the significance of proper nutrient and tillage methods by focusing on the intricate relationships between agronomic techniques and environmental factors that shape barley’s nutritional profile. Full article

Greenhouses are applied to mitigate the deleterious effects of inclement weather, which facilitates the optimal growth and development of the crops. South China has a climate characterized by high temperature and high humidity, and the temperature and relative humidity inside a Venlo greenhouse are higher than those in the atmosphere. In this paper, the numerical model of the flow distribution of a Venlo greenhouse in South China was established using the CFD method, which mainly applied the DO model, the k-e turbulence model, and the porous medium model. The porous resistance characteristics of tomatoes were obtained through experimental research. The inertial resistances of tomato plants in the x, y, and z directions were 80,000,000, 18,000,000, and 120,000,000, respectively; the viscous resistances of tomato plants in the x, y, and z directions were 0.43, 0.60, and 0.63, respectively. The porosity of tomato plants was 0.996. The average difference between the temperature of the established numerical model and the experimental temperature was less than 0.11 °C, and the average relative error was 2.72%. This research also studied the effects of five management and structure parameters on the velocity and temperature distribution in a greenhouse. The optimal inlet velocity is 1.32 m/s, with the COF of velocity and temperature being 9.23% and 1.18%, respectively. The optimal skylight opening is 1.76 m, with the COF of velocity and temperature being 10.68% and 0.88%, respectively. The optimal side window opening is 0.67 m, with the COF of velocity and temperature being 9.25% and 2.10%, respectively. The optimal side window height is 1.18 m, with the COF of velocity and temperature being 9.50% and 1.33%, respectively. The optimal planting interval is 1.40 m, with the COF of velocity and temperature being 15.29% and 0.20%, respectively. The results provide a reference for the design and management of Venlo greenhouses in South China. Full article

Aiming at the problems of the high multiple- and missed-seeding index and low operation efficiency of current mechanical potato seed-meters in minituber sowing, this study designed an air-suction roller-type minituber seed-metering device for minitubers (mass between 2 and 4 g) in accordance with the agronomic standards for potato cultivation in the single-cropping area of northern China. An account of the device’s structure and operational principle was made, its working process was theoretically analysed, and the three main factors affecting the airflow suction were determined: the seed roller speed, the suction seeding hole diameter, and the air inlet negative pressure. This study adopted the fluid dynamics simulation method and determined that the ideal location of the air inlet was 30° for horizontal inclination and 60° for vertical inclination. Then, based on the CFD-DEM fluid-structure coupling simulation method, the impact of a range of factors on the functionality of the seed-metering device under different conditions was studied and verification tests were carried out. Design-Expert was used to analyse test results. The results showed that when the pressure at the air inlet was −7000 Pa, the speed of the seeding roller was 40.2 r·min⁻¹, the suction seeding hole diameter was 10.37 mm, and the performance was optimal: the qualified index was 92.95%, the multiple-seeding index was 4.16%, and the missed-seeding index was 2.89%. The research results show that the seed-metering device developed under this scheme exhibited satisfactory seeding performance during operation and was able to meet the demands of minituber sowing. Full article

Monitoring crop damage caused by wildlife remains a significant challenge in agricultural management, particularly in the case of large-scale monocultures such as maize. The given study presents a semi-automated process for detecting wildlife-induced damage using RGB imagery acquired from unmanned aerial vehicles (UAVs). The method is designed for non-specialist users and is fully integrated within the QGIS platform. The proposed approach involves calculating three vegetation indices—Excess Green (ExG), Green Leaf Index (GLI), and Modified Green-Red Vegetation Index (MGRVI)—based on a standardized orthomosaic generated from RGB images collected via UAV. Subsequently, an unsupervised k-means clustering algorithm was applied to divide the field into five vegetation vigor classes. Within each class, 25% of the pixels with the lowest average index values were preliminarily classified as damaged. A dedicated QGIS plugin enables drone data analysts (Drone Data Analysts—DDAs) to adjust index thresholds, based on visual interpretation, interactively. The method was validated on a 50-hectare maize field, where 7 hectares of damage (15% of the area) were identified. The results indicate a high level of agreement between the automated and manual classifications, with an overall accuracy of 81%. The highest concentration of damage occurred in the “moderate” and “low” vigor zones. Final products included vigor classification maps, binary damage masks, and summary reports in HTML and DOCX formats with visualizations and statistical data. The results confirm the effectiveness and scalability of the proposed RGB-based procedure for crop damage assessment. The method offers a repeatable, cost-effective, and field-operable alternative to multispectral or AI-based approaches, making it suitable for integration with precision agriculture practices and wildlife population management. Full article

Computer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput. In orchards specially constructed for variety testing or breeding programs, computer vision tools should be able to extract phenotypical information form each tree separately. We focus on segmenting individual apple trees as the main task in this context. Segmenting individual apple trees in dense orchard rows is challenging because of the complexity of outdoor illumination and intertwined branches. Traditional methods rely on supervised learning, which requires a large amount of annotated data. In this study, we explore an alternative approach using prompt engineering with the Segment Anything Model and its variants in a zero-shot setting. Specifically, we first detect the trunk and then position a prompt (five points in a diamond shape) located above the detected trunk to feed to the Segment Anything Model. We evaluate our method on the apple REFPOP, a new large-scale European apple tree dataset and on another publicly available dataset. On these datasets, our trunk detector, which utilizes a trained YOLOv11 model, achieves a good detection rate of $97\%$ based on the prompt located above the detected trunk, achieving a Dice score of 70% without training on the REFPOP dataset and 84% without training on the publicly available dataset.We demonstrate that our method equals or even outperforms purely supervised segmentation approaches or non-prompted foundation models. These results underscore the potential of foundational models guided by well-designed prompts as scalable and annotation-efficient solutions for plant segmentation in complex agricultural environments. Full article

Early crop yield prediction is a major challenge in precision agriculture, and efficient and rapid yield prediction is highly important for sustainable fruit production. The accurate detection of major fruit characteristics, including flowering, green fruiting, and ripening stages, is crucial for early yield estimation. Currently, most crop yield estimation studies based on the YOLO model are only conducted during a single stage of maturity. Combining multi-growth period data for crop analysis is of great significance for crop growth detection and early yield estimation. In this study, a new network model, YOLOv8-RL, was proposed using citrus multigrowth period characteristics as a data source. A citrus yield estimation model was constructed and validated by combining network identification counts with manual field counts. Compared with YOLOv8, the number of parameters of the improved network is reduced by 50.7%, the number of floating-point operations is decreased by 49.4%, and the size of the model is only 3.2 MB. In the test set, the average recognition rate of citrus flowers, green fruits, and orange fruits was 95.6%, the mAP@.5 was 94.6%, the FPS value was 123.1, and the inference time was only 2.3 milliseconds. This provides a reference for the design of lightweight networks and offers the possibility of deployment on embedded devices with limited computational resources. The two estimation models constructed on the basis of the new network had coefficients of determination R² values of 0.91992 and 0.95639, respectively, with a prediction error rate of 6.96% for citrus green fruits and an average error rate of 3.71% for orange fruits. Compared with network counting, the yield estimation model had a low error rate and high accuracy, which provided a theoretical basis and technical support for the early prediction of fruit yield in complex environments. Full article

The integration of digital twin technology with robotic automation holds significant promise for advancing sustainable horticulture in controlled environment agriculture. This article presents DigiHortiRobot, a novel AI-driven digital twin architecture tailored for hydroponic greenhouse systems. The proposed framework integrates real-time sensing, predictive modeling, task planning, and dual-arm robotic execution within a modular, IoT-enabled infrastructure. DigiHortiRobot is structured into three progressive implementation phases: (i) monitoring and data acquisition through a multimodal perception system; (ii) decision support and virtual simulation for scenario analysis and intervention planning; and (iii) autonomous execution with feedback-based model refinement. The Physical Layer encompasses crops, infrastructure, and a mobile dual-arm robot; the virtual layer incorporates semantic modeling and simulation environments; and the synchronization layer enables continuous bi-directional communication via a nine-tier IoT architecture inspired by FIWARE standards. A robot task assignment algorithm is introduced to support operational autonomy while maintaining human oversight. The system is designed to optimize horticultural workflows such as seeding and harvesting while allowing farmers to interact remotely through cloud-based interfaces. Compared to previous digital agriculture approaches, DigiHortiRobot enables closed-loop coordination among perception, simulation, and action, supporting real-time task adaptation in dynamic environments. Experimental validation in a hydroponic greenhouse confirmed robust performance in both seeding and harvesting operations, achieving over 90% accuracy in localizing target elements and successfully executing planned tasks. The platform thus provides a strong foundation for future research in predictive control, semantic environment modeling, and scalable deployment of autonomous systems for high-value crop production. Full article

Wheat is one of the important grain crops, and spike counting is crucial for predicting spike yield. However, in complex farmland environments, the wheat body scale has huge differences, its color is highly similar to the background, and wheat ears often overlap with each other, which makes wheat ear detection work face a lot of challenges. At the same time, the increasing demand for high accuracy and fast response in wheat spike detection has led to the need for models to be lightweight function with reduced the hardware costs. Therefore, this study proposes a lightweight wheat ear detection model, CML-RTDETR, for efficient and accurate detection of wheat ears in real complex farmland environments. In the model construction, the lightweight network CSPDarknet is firstly introduced as the backbone network of CML-RTDETR to enhance the feature extraction efficiency. In addition, the FM module is cleverly introduced to modify the bottleneck layer in the C2f component, and hybrid feature extraction is realized by spatial and frequency domain splicing to enhance the feature extraction capability of wheat to be tested in complex scenes. Secondly, to improve the model’s detection capability for targets of different scales, a multi-scale feature enhancement pyramid (MFEP) is designed, consisting of GHSDConv, for efficiently obtaining low-level detail information and CSPDWOK for constructing a multi-scale semantic fusion structure. Finally, channel pruning based on Layer-Adaptive Magnitude Pruning (LAMP) scoring is performed to reduce model parameters and runtime memory. The experimental results on the GWHD2021 dataset show that the ${\mathrm{AP}}_{50}$ of CML-RTDETR reaches 90.5%, which is an improvement of 1.2% compared to the baseline RTDETR-R18 model. Meanwhile, the parameters and GFLOPs have been decreased to 11.03 M and 37.8 G, respectively, resulting in a reduction of 42% and 34%, respectively. Finally, the real-time frame rate reaches 73 fps, significantly achieving parameter simplification and speed improvement. Full article

Leaf chlorophyll content (LCC), leaf area index (LAI), and above-ground biomass (AGB) are important growth parameters for characterizing potato growth and predicting yield. While deep learning has demonstrated remarkable advancements in estimating crop growth parameters, the limited availability of field data often compromises model accuracy and generalizability, impeding large-scale regional applications. This study proposes a novel deep learning model that integrates multi-task learning and few-shot learning to address the challenge of low data in growth parameter prediction. Two multi-task learning architectures, MTL-DCNN and MTL-MMOE, were designed based on deep convolutional neural networks (DCNNs) and multi-gate mixture-of-experts (MMOE) for the simultaneous estimation of LCC, LAI, and AGB from Sentinel-2 imagery. Building on this, a few-shot learning framework for growth prediction (FSLGP) was developed by integrating simulated spectral generation, model-agnostic meta-learning (MAML), and meta-transfer learning strategies, enabling accurate prediction of multiple growth parameters under limited data availability. The results demonstrated that the incorporation of calibrated simulated spectral data significantly improved the estimation accuracy of LCC, LAI, and AGB (R² = 0.62~0.73). Under scenarios with limited field measurement data, the multi-task deep learning model based on few-shot learning outperformed traditional mixed inversion methods in predicting potato growth parameters (R² = 0.69~0.73; rRMSE = 16.68%~28.13%). Among the two architectures, the MTL-MMOE model exhibited superior stability and robustness in multi-task learning. Independent spatiotemporal validation further confirmed the potential of MTL-MMOE in estimating LAI and AGB across different years and locations (R² = 0.37~0.52). These results collectively demonstrated that the proposed FSLGP framework could achieve reliable estimation of crop growth parameters using only a very limited number of in-field samples (approximately 80 samples). This study can provide a valuable technical reference for monitoring and predicting growth parameters in other crops. Full article

Given the importance of potatoes in Brazilian agribusiness and the need to establish sustainable production systems, interest has increased in the implementation of more efficient fertilization methods for the cultivation. Thus, the objective of this study was to evaluate the response of the cultivars Ágata and Atlantic to fertilization with a pelleted organomineral source in comparison to conventional fertilization performed with a mineral source. A causal block design was used with five treatments [100% of the recommendation for fertilization with mineral sources 03-35-06; and 100%, 80%, 60%, and 40% of the recommended dose with organomineral fertilizer (02-20-05)] in four replications, totaling 20 plots. The application of the organomineral in plant fertilization can be an interesting source of fertilizer for the cultivation of Ágata and Atlantic potatoes and can be applied with dose adjustments. For the cultivar Ágata, the doses of 100% and 80% organomineral fertilizer together with mineral fertilization resulted in the highest total yields. The lower doses (60% and 40%) made it possible to obtain a higher percentage of special potatoes, considered to be of the highest commercial value, than 80% of the organomineral fertilizers and 100% mineral standard. For the Atlantic cultivar, the total yield responses to organomineral were like those obtained with exclusively mineral fertilization. These findings indicate that organomineral fertilizers can be used efficiently with adjusted doses, maintaining productivity and tuber quality while potentially reducing fertilizer input costs and environmental impacts, contributing to more sustainable potato cropping systems. Full article