Search Results (512)

Automated cherry harvesting presents a significant opportunity to overcome the high costs and inefficiencies of manual labor in modern agriculture. However, robotic harvesting in dense canopies requires sophisticated path planning to navigate cluttered branches and selectively pick target fruits. This paper introduces a complete robotic harvesting solution centered on a novel path-planning algorithm: the Multi-Strategy Integrated RRT for Continuous Harvesting Path (MSI-RRTCHP) algorithm. Our system first employs a machine vision system to identify and locate mature cherries, distinguishing them from unripe fruits, leaves, and branches, which are treated as obstacles. Based on this visual data, the MSI-RRTCHP algorithm generates an optimal picking trajectory. Its core innovation is a synergistic strategy that enables intelligent navigation by combining probability-guided exploration, goal-oriented sampling, and adaptive step size adjustments based on the obstacle’s density. To optimize the picking sequence for multiple targets, we introduce an enhanced traversal algorithm ($\sigma$-TSP) that accounts for obstacle interference. Field experiments demonstrate that our integrated system achieved a 90% picking success rate. Compared with established algorithms, the MSI-RRTCHP algorithm reduced the path length by up to 25.47% and the planning time by up to 39.06%. This work provides a practical and efficient framework for robotic cherry harvesting, showcasing a significant step toward intelligent agricultural automation. Full article

In modern agriculture, tractors play a crucial role in powering tools and implements. Proper operation of agricultural tractors in mechanized field operations can support sustainable agriculture and reduce emissions of pollutants such as carbon dioxide (CO₂). This has been a recurring concern associated with agricultural intensification for food production. This study aimed to evaluate the optimization of tractor gears and engine speed during crop operations to minimize CO₂ emissions and promote sustainability. The experiment was conducted using a strip plot design with subdivided sections and six replications, following a double factorial structure. The first factor evaluated was the type of agricultural implement (disc harrow, subsoiler, or sprayer), while the second factor was the engine speed setting (nominal or reduced). Operational and energy performance metrics were analyzed, including fuel consumption and CO₂ emissions, travel speed, effective working time, wheel slippage, and working depth. Optimized gear selection and engine speeds resulted in a 20 to 40% reduction in fuel consumption and CO₂ emissions. However, other evaluated parameters remain unaffected by the reduced engine speed, regardless of the implement used, ensuring the operation’s quality. Thus, optimizing operator training or configuring machines allows for environmental impact reduction, making agricultural practices more sustainable. 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

The adoption of digital technologies, such as the Internet of Things (IoT), has emerged as a key strategy to improve efficiency, sustainability, and productivity in the agricultural sector, especially in contexts of modernization and digital transformation in developing regions. This study analyzes the key factors influencing the adoption of IoT technologies by farmers in the province of Guayas, Ecuador, and their impact on agricultural yields. The research is grounded in innovation diffusion theory and technology acceptance models, which emphasize the role of perception, usability, training, and economic viability in digital adoption. A total of 250 surveys were administered, with 232 valid responses (92.8% response rate), reflecting strong interest from the agricultural sector in digital transformation and precision agriculture. Using structural equation modeling (SEM), the results confirm that general perception of IoT (β = 0.514), practical functionality (β = 0.488), and technical training (β = 0.523) positively influence adoption, while high implementation costs negatively affect it (β = −0.651), all of which are statistically significant (p < 0.001). Furthermore, adoption has a strong positive effect on agricultural yield (β = 0.795). The model explained a high percentage of variance in both adoption (R² = 0.771) and performance (R² = 0.706), supporting its predictive capacity. These findings underscore the need for public and private institutions to implement targeted training and financing strategies to overcome economic barriers and foster the sustainable integration of IoT technologies in Ecuadorian agriculture. Full article

The search for sustainable, economically viable, and effective plant protection strategies against pathogenic bacteria, fungi, and viruses is a major challenge in modern agricultural practices. Chitosan (CS) is an abundant cationic natural biopolymer known for its biocompatibility, low toxicity, and antimicrobial properties. Its potential use in agriculture for pathogen control is a promising alternative to traditional chemical fertilisers and pesticides, which raise concerns regarding public health, environmental protection, and pesticide resistance. This study focused on the preparation of chitosan nanoparticles (CS-NPs) through cross-linking with organic molecules, such as tannic acid (TA). Various formulations were explored for the development of stable nanoscale particles having encapsulation capabilities towards low compounds of varying polarity and with potential agricultural applications relevant to plant health and growth. The solution properties of the NPs were assessed using dynamic and electrophoretic light scattering (DLS and ELS); their morphology was observed through atomic force microscopy (AFM), while analytical ultracentrifugation (AUC) measurements provided insights into their molar mass. Their properties proved to be primarily influenced by the concentration of CS, which significantly affected its intrinsic conformation. Additional structural insights were obtained via infrared and UV–Vis spectroscopic measurements, while detailed fluorescence analysis with the use of three different probes, as model cargo molecules, provided information regarding the hydrophobic and hydrophilic microdomains within the particles. Full article

Environmentally friendly coated urea prepared using a waterborne polymer coating material is essential for promoting green and sustainable practices in modern agriculture. However, significant efforts are still urgently needed to address the undesirable properties of waterborne polymer coatings, i.e., poor hydrophobic properties and numerous micropores. Herein, dual nano-SiO₂ and siloxane-modified waterborne-polymer-coated urea was successfully developed. The characteristics of waterborne-polymer-coated urea before and after modification were compared. The results demonstrate that nano-SiO₂ and siloxane modification improved the hydrophobicity (water absorption decreased from 119.86% to 46.35%) and mechanical strength (tensile strength increased from 21.09 to 31.29 MPa, and the elongation at break exhibited an increase of 22.42%) of the waterborne polymer coatings. Furthermore, the –OH number of the modified coatings was decreased, while the coating surface formed a nano-scale rough structure, prolonging the nitrogen (N)-controlled release period from 7 to 28 days. Overall, the proposed novel dual-modification technique utilizing waterborne polymer coatings highlights the significant potential of eco-friendly coated urea with renewable coatings in modern agriculture. Full article

Biochar is an amendment that can enhance both soil fertility and sequester carbon. However, its historical applications continue to be underexplored. In this overview, we investigate the formiguer method of burning woody biomass to create agricultural biochar for use as fertilizer in Catalonia, Spain, within the context of historical biochar use. A literature review targeted searches of scholarly databases to compare the formiguer method to Amazonian terra preta and other traditional biochar use. We identified sources covering biochar properties, soil impacts, and historical agricultural practices within the Iberian Peninsula and briefly described the main methods or treatments used during this process. Past research demonstrates that the formiguer method, which involves pyrolytic combustion of biomass within soil mounds, improves microbial activity, increases soil phosphorus and potassium availability from soil structure, and leads to long-term carbon stabilization, even though it can result in short-term decreases in soil organic carbon and nitrogen losses. Despite being abandoned in Europe with the rise of chemical fertilizers, the use of formiguers exemplifies a decentralized approach to nutrient and agroecosystem management. The literature highlights the relevance that these traditional biochar practices can have in informing modern soil management and sustainable agricultural strategies. Understanding the formiguer can offer critical insights to optimize contemporary biochar applications and historical techniques into future sustainability frameworks. Full article

This research aims to analyze the evolution of productivity and efficiency in tractors featuring varying propulsion system designs through the development of a parametric modeling approach. Recognizing that large row-crop tractors represent a significant capital investment—ranging from USD 0.4 to over 0.8 million for current-generation models—and that machinery costs constitute a substantial share of farm production expenses, this study addresses the urgent need for data-driven decision-making in agricultural enterprises. Utilizing consolidated OECD Code 2 tractor test data for all large row-crop John Deere tractors from the MFWD era to the latest generation, the study evaluates tractor performance across multiple productivity and efficiency indicators. The analysis culminates in the creation of a robust, user-friendly parametric model (R² = 0.9337, RMSE = 1.0265), designed to assist stakeholders in making informed decisions regarding tractor replacement or upgrading. By enabling the optimization of productivity and efficiency while accounting for agronomic and timeliness constraints, this model supports sustainable and profitable management practices in modern agriculture. Full article

In recent years, agricultural crops have increasingly been attacked by more destructive insect pests, forcing modern farming to depend mainly on chemical insecticides. Although valuable, their widespread and intensive misuse has raised serious concerns about environmental and public health impacts. RNAi has been proposed as a safer alternative due to its high specificity, adaptability, and low ecological footprint. So far, dsRNA has proven effective in controlling various pest species, either through topical application or via genetically modified plants. Despite advances, large-scale implementation of RNAi remains challenging due to technical and biological hurdles that contribute to inconsistent performance. Key aspects such as dsRNA design, delivery techniques, and cellular uptake mechanisms still require refinement. Additionally, ensuring environmental stability, addressing biosafety concerns, and developing cost-effective production methods are essential for its practical application. In this review, we explore recent advances in the design and implementation of dsRNA, as well as the strategies that could support the successful integration of RNAi technology into pest management programs. Full article

In the context of efforts to reduce greenhouse gas emissions in the building sector, the reintegration of traditional earthen construction into modern architectural and renovation practices offers a sustainable alternative. To address the mechanical and water-resistance limitations of adobe bricks, the use of agricultural waste—such as rice husk—is increasingly being explored. This experimental study evaluates the effects of rice husk addition on the mechanical, hygrothermal, and acoustic properties of adobe bricks. Two soil types—one siliceous and one calcareous—were combined with 1, 2, and 3 wt% rice husk to produce bio-based earthen bricks. The influence of rice husk was found to depend strongly on the soils’ mineralogical and granulometric characteristics. The most significant improvements were in hygrothermal performance: at 3 wt%, thermal conductivity was reduced by up to 35% for calcareous soil and 20% for siliceous soil, indicating enhanced insulation. Specific heat capacity also increased with husk content, suggesting better thermal inertia. The moisture buffering capacity, already high in raw soils, is further improved due to increased surface porosity. Mechanically, rice husk incorporation had mixed effects: a modest increase in compressive strength was observed in siliceous soil at 1 wt%, while calcareous soil showed slight improvement at 3 wt%. Acoustic performance remained low across all samples, with minimal gains attributed to limited macro-porosity. These findings highlight the importance of soil composition in optimizing rice husk dosage and suggest promising potential for rice husk-stabilized adobe bricks, especially in thermally demanding environments. Full article

In recent years, heavy metal contamination in agricultural products has become a growing concern in the field of food safety. Copper (Cu) stress in crops not only leads to significant reductions in both yield and quality but also poses potential health risks to humans. This study proposes an efficient and precise non-destructive detection method for Cu stress in oilseed rape, which is based on hyperspectral false-color image construction using principal component analysis (PCA). By comprehensively capturing the spectral representation of oilseed rape plants, both the one-dimensional (1D) spectral sequence and spatial image data were utilized for multi-class classification. The classification performance of models based on 1D spectral sequences was compared from two perspectives: first, between machine learning and deep learning methods (best accuracy: 93.49% vs. 96.69%); and second, between shallow and deep convolutional neural networks (CNNs) (best accuracy: 95.15% vs. 96.69%). For spatial image data, deep residual networks were employed to evaluate the effectiveness of visible-light and false-color images. The RegNet architecture was chosen for its flexible parameterization and proven effectiveness in extracting multi-scale features from hyperspectral false-color images. This flexibility enabled RegNetX-6.4GF to achieve optimal performance on the dataset constructed from three types of false-color images, with the model reaching a Macro-Precision, Macro-Recall, Macro-F₁, and Accuracy of 98.17%, 98.15%, 98.15%, and 98.15%, respectively. Furthermore, Grad-CAM visualizations revealed that latent physiological changes in plants under heavy metal stress guided feature learning within CNNs, and demonstrated the effectiveness of false-color image construction in extracting discriminative features. Overall, the proposed technique can be integrated into portable hyperspectral imaging devices, enabling real-time and non-destructive detection of heavy metal stress in modern agricultural practices. Full article

Soilless cultivation technology is a key means of overcoming traditional agricultural resource limits, providing an important path to efficient and sustainable modern agriculture by precisely regulating crop rhizospheric environments. This paper systematically reviews the technical system of soilless cultivation, nutrient solution management strategies, the interaction mechanism of rhizosphere microorganisms, and future development directions, aiming to reveal its technical advantages and innovation potential. This review shows that solid and non-solid substrate cultivation improves resource utilization efficiency and yield, but substrate sustainability and technical cost need urgent attention. The dynamic regulation of nutrient solution and intelligent management can significantly enhance nutrient absorption efficiency. Rhizosphere microorganisms directly regulate crop health through nitrogen fixation, phosphorus solubilization, and pathogen antagonism. However, the community structure and functional stability of rhizosphere microorganisms in organic systems are prone to imbalance, requiring targeted optimization via synthetic biology methods. Future research should focus on the development of environmentally friendly substrates, the construction of intelligent environmental control systems, and microbiome engineering to promote the expansion of soilless cultivation towards low-carbon, precise, and spatial directions. This paper systematically references the theoretical improvements and practical innovations in soilless cultivation technology, facilitating its large-scale application in food security, ecological protection, and resource recycling. Full article

Wheat disease detection is a crucial component of intelligent agricultural systems in modern agriculture. However, at present, its detection accuracy still has certain limitations. The existing models hardly capture the irregular and fine-grained texture features of the lesions, and the results of spatial information reconstruction caused by standard upsampling operations are inaccuracy. In this work, the GDFC-YOLO method is proposed to address these limitations and enhance the accuracy of detection. This method is based on YOLOv11 and encompasses three key aspects of improvement: (1) a newly designed Ghost Dynamic Feature Core (GDFC) in the backbone, which improves the efficiency of disease feature extraction and enhances the model’s ability to capture informative representations; (2) a redesigned neck structure, Disease-Focused Neck (DF-Neck), which further strengthens feature expressiveness, to improve multi-scale fusion and refine feature processing pipelines; and (3) the integration of the Powerful Intersection over Union v2 (PIoUv2) loss function to optimize the regression accuracy and convergence speed. The results showed that GDFC-YOLO improved the average accuracy from 0.86 to 0.90 when the cross-overmerge threshold was 0.5 (mAP@0.5), its accuracy reached 0.899, its recall rate reached 0.821, and it still maintained a structure with only 9.27 M parameters. From these results, it can be known that GDFC-YOLO has a good detection performance and stronger practicability relatively. It is a solution that can accurately and efficiently detect crop diseases in real agricultural scenarios. Full article

Efficient weed detection in dryland spring wheat fields is crucial for sustainable agriculture, as it enables targeted interventions that reduce herbicide use, minimize environmental impact, and optimize resource allocation in water-limited farming systems. This paper presents HSG-Net, a novel lightweight object detection model based on YOLOv8 for weed identification in dryland spring wheat fields. The proposed architecture integrates three key innovations: an HGNetv2 backbone for efficient feature extraction, C2f-S modules with star-shaped attention mechanisms for enhanced feature representation, and Group Head detection heads for parameter-efficient prediction. Experiments on a dataset of eight common weed species in dryland spring wheat fields show that HSG-Net improves detection accuracy while cutting computational costs, outperforming modern deep learning approaches. The model effectively addresses the unique challenges of weed detection in dryland agriculture, including visual similarity between crops and weeds, variable illumination conditions, and complex backgrounds. Ablation studies confirm the complementary contributions of each architectural component, with the full HSG-Net model achieving an optimal balance between accuracy and resource efficiency. The lightweight nature of HSG-Net makes it particularly suitable for deployment on resource-constrained devices used in precision agriculture, enabling real-time weed detection and targeted intervention in field conditions. This work represents an important advancement in developing practical deep learning solutions for sustainable weed management in dryland farming systems. Full article

South Africa’s bioeconomy strategy identifies bio-innovation as a key driver of economic growth and social development, with plant breeding playing a central role in improving food security through the development of high-yielding, resilient, and high-quality crops. However, consumer perceptions of recent advances, particularly new breeding techniques (NBTs), remain underexplored. This study examines South African consumer attitudes towards plant breeding innovations, using a mixed-methods approach. The initial focus group interviews informed the development of a structured quantitative survey examining familiarity, perceptions, and acceptance of plant breeding technologies. Consumer awareness of plant breeding principles was found to be limited, with 67–68% of respondents unfamiliar with both conventional and modern plant breeding procedures. Despite this information gap, consumers expressed conditional support for modern breeding techniques, especially when associated with actual benefits like increased nutritional value, environmental sustainability, and crop resilience. When favourable effects were outlined, support for general investment in modern breeding practices climbed from 45% to 74%. Consumer purchase decisions emphasised price, product quality, and convenience over manufacturing techniques, with sustainability ranked last among the assessed factors. Trust in the sources of food safety information varied greatly, with medical experts and scientists being ranked highly, while government sources were viewed more sceptically. The results further suggest that targeted education could improve customer confidence, as there is a significant positive association (R² = 0.938) between familiarity and acceptance. These findings emphasise the significance of open communication strategies and focused consumer education in increasing the adoption of plant breeding breakthroughs. The study offers useful insights for policymakers, researchers, and industry stakeholders working on engagement strategies to facilitate the ethical growth and application of agricultural biotechnology in support of food security and quality in South Africa. This study contributes to a better understanding of South African consumers’ perceptions of plant breeding innovations and food safety. The research findings offer valuable insights for policymakers, researchers, and industry stakeholders in developing effective engagement and communication strategies that address consumer concerns and promote the adoption of products derived from diverse plant breeding technologies. Full article