Search Results (1,357)

Agriculture is being transformed through automation and robotics to improve efficiency and reduce production costs. However, this transformation poses risks of job loss, particularly for low-skilled workers, as automation decreases the need for human labor. To adapt, the workforce must acquire new qualifications to collaborate with automated systems or shift to roles that leverage their unique human abilities. In this study, 15 agricultural occupations were methodically mapped in a cognitive/manual versus routine/non-routine two-dimensional space. Subsequently, each occupation’s susceptibility to robotization was assessed based on the readiness level of existing technologies that can automate specific tasks and the relative importance of these tasks in the occupation’s execution. The qualifications required for occupations less impacted by robotization were summarized, detailing the specific knowledge, skills, and work styles required to effectively integrate the emerging technologies. It was deduced that occupations involving primary manual routine tasks exhibited the highest susceptibility rate, whereas occupations with non-routine tasks showed lower susceptibility. To thrive in this evolving landscape, a strategic combination of STEM (science, technology, engineering, and mathematics) skills with essential management, soft skills, and interdisciplinary competences is imperative. Finally, this research stresses the importance of strategic preparation by policymakers and educational systems to cultivate key competencies, including digital literacy, that foster resilience, inclusivity, and sustainability in the sector. Full article

Computational fluid dynamics (CFD) is a numerical tool often used to predict anticipated observations using only the physics involved by numerically solving the conservation equations for energy, momentum, and continuity. These governing equations have been around for more than one hundred years, but only limited analytical solutions exist for specific geometries and conditions. CFD provides a numerical solution to these governing equations, and several commercial software and shareware versions exist that provide numerical solutions for customized geometries requiring solutions. Often, experiments are cost prohibitive and/or time consuming, or cannot even be performed, such as the explosion of a chemical plant, downwind air concentrations and the impact on residents and animals, contamination in a river from a point source loading following a train derailment, etc. A modern solution to these problems is the use of CFD to digitally evaluate the output for a given scenario. This paper discusses the use of CFD at Corteva and offers a flavor of the types of problems that can be solved in agricultural manufacturing for pesticides and environmental scenarios in which pesticides are used. Only a handful of examples are provided, but there is a near semi-infinite number of future possibilities to consider. Full article

The real-time detection of fruits and plants is a crucial aspect of digital agriculture, enhancing farming efficiency and productivity. This study addresses the challenge of embedding a real-time strawberry detection system in a small mobile robot operating within a greenhouse environment. The embedded system is based on the YOLO architecture running in a single GPU card, with the Open Neural Network Exchange (ONNX) representation being employed to accelerate the detection process. The experiments conducted in this study demonstrate that the proposed model achieves a mean average precision (mAP) of over 97%, processing eight frames per second for 512 × 512 pixel images. These results affirm the utility of the proposed approach in detecting strawberry plants in order to optimize the spraying process and avoid inflicting any harm on the plants. The goal of this research is to highlight the potential of integrating advanced detection algorithms into small-scale robotics, providing a viable solution for enhancing precision agriculture practices. Full article

This study explores the potential of micro-credentialing and digital badges in developing and validating the knowledge, skills, and other attributes (KSaOs) required for diverse Remotely Piloted Aircraft Systems (RPAS) operations. The rapid proliferation of drone usage has outpaced the development of necessary KSaOs for safe and efficient drone operations. This research aims to bridge this gap by identifying the unique and specific KSaOs required for different types of drone operations and examining how micro-credentialing and digital badges can provide tangible evidence of these KSaOs. The study also investigates the potential benefits and challenges of implementing digital badges in the RPAS sector and how these challenges can be addressed. Furthermore, it explores how digital badges can contribute to the standardization and recognition of RPAS competencies across different national regulatory bodies. The methodology involves observational studies of publicly available videos of drone operations, with a focus on agriculture spraying operations. The findings highlight the importance of both generic and specific KSaOs in RPAS operations and suggest that digital badges may provide an effective means of evidencing mastery of these competencies. This research contributes to the ongoing discourse on drone regulation and competency development, offering practical insights for regulators, training providers, and drone operators. Full article

This study focuses on the promising use of biospeckle technology to detect water stress in plants, a complex physiological mechanism. This involves monitoring the temporal activity of biospeckle pattern to study the occurrence of stress within the leaf. The effects of water stress in plants can involve physical and biochemical changes. Some of these changes may alter the optical scattering properties of leaves. The present study therefore proposes to test the potential of a biospeckle measurement to observe the temporal evolution in different varieties of sunflower plants under water stress. An experiment applying controlled water stress with osmotic shock using polyethylene glycol 6000 (PEG) was conducted on two sunflower varieties: one sensitive, and the other more tolerant to water stress. Temporal monitoring of biospeckle activity in these plants was performed using the average value of difference (AVD) indicator. Results indicate that AVD highlights the difference in biospeckle activity between day and night, with lower activity at night for both varieties. The addition of PEG entailed a gradual decrease in values throughout the experiment, particularly for the sensitive variety. The results obtained are consistent with the behaviour of the varieties submitted to water stress. Indeed, a few days after the introduction of PEG, a stronger decrease in AVD indicator values was observed for the sensitive variety than for the resistant variety. This study highlights the dynamics of biospeckle activity for different sunflower varieties undergoing water stress and can be considered as a promising phenotyping tool. Full article

Water is an essential resource for both rural and agricultural areas; it can be wisely distributed and used in the field to protect daily life, production, the natural environment, and the safety and stability of regional drainage and flood control systems. Our research selected a typical plains rural river network area with agriculture as the main industry to investigate the most effective method of farmland diversion and drainage. We comprehensively planned and transformed the water system flow, water conservation engineering, and the ecological environment in the irrigation area through the reutilization system. The reutilization system’s operation and scheduling design is implemented for four specific periods: the water replenishment cycle, agricultural irrigation, agricultural drainage and the rainy period of the flood season. The research period ranges from 2020 to 2023 after the completion of the system. We used monitoring, the recording of hydraulic equipment parameters and data collection to evaluate the balance of water supply and demand in the study area. At the same time, we have tracked and evaluated the four aspects of water quality enhancement, water conservation and flood control, and agricultural irrigation. The results show that the total agricultural water consumption decreased by 2.9%, and the amount of water saved increased by 9.6%. The current segment creates the rivers’ embankment standards. With a 92% irrigation guarantee rate, the current section forms and the embankment standards of the rivers satisfy the design storage volume and the flood level of one in twenty years. The water quality of all the rivers in the area has decreased by 5~10% compared to the average concentration prior to establishment. This study verifies the comprehensive effect and the suitability of the system by comparing the before and after effects, and provides a scientific basis for the method of efficient recycling and utilization of water resources in the rural plains river network area; we also propose the guidance of increasing the digital twin control and long-term operation mechanism to ensure the long-term stable operation of the technology. Full article

Brazil is the second largest beef producer and a leading exporter, contributing to some 3000 t CWE in global markets (27.7% of market share). The sector has experienced substantial development, but yields remain far below potential, and there are growing concerns regarding land use change and greenhouse gas emissions. The need for sustainable technologies, such as sound pasture management and integrated farming systems, is evident, but adoption may be low amongst farmers unable to keep up with technological advances. This article describes the historical developments of Brazilian beef farming towards sustainability and discusses possible socioenvironmental outcomes. We combined an extensive literature review, public data, and our own insights as senior researchers to achieve that. The trajectory shown here evidenced the technological intensification of Brazilian beef farming, with strong support of public policies for decarbonizing agriculture. Nonetheless, the pace of this transition may affect small to medium farmers with limited access to information, technologies, and credit. Our recommendations involve a broad program of technical assistance and training on sustainable technologies, including financial and digital literacy. A novel approach to financing farmers is suggested to support a sustainable and inclusive transition in beef farming in Brazil. Full article

In the current study, an index system for digital agriculture growth was constructed. The index encompasses six key dimensions, namely production, operation, service, management, sustainability, and digital information infrastructure. Data from 30 Chinese provinces between 2011 and 2020 were collected and analyzed using the entropy method, Moran index, Dagum’s Gini coefficient, and the kernel density estimate. An in-depth analysis of the development level and spatial patterns, dynamic evolution and intra- and inter-regional differences in China (i.e., eastern, western, and central regions) was conducted. From the result, an overall growing trend of digital agriculture in China was observed, with a relatively more advanced status in the eastern region. A positive spatial dependence, showing a “high-high” and “low-low” (HH, LL) trend, was obtained. However, the regional spatial dependence has generally weakened since 2019. The intra-regional differences were large in western and eastern areas, while the greatest inter-regional differences were unveiled between western and eastern regions. The country’s overall differences mainly stemmed from inter-regional differences. The overall kernel density curves moved to the right over time, showing a trend of a gradual rise in digital agricultural growth, accompanied by a polarization pattern in the western region. Full article

Wheat powdery mildew is a fungal disease that significantly impacts wheat yield and quality. Controlling this disease requires the use of resistant varieties, fungicides, crop rotation, and proper sanitation. Precision agriculture focuses on the strategic use of agricultural inputs to maximize benefits while minimizing environmental and human health effects. Object detection using computer vision enables selective spraying of pesticides, allowing for targeted application. Traditional detection methods rely on manually crafted features, while deep learning-based methods use deep neural networks to learn features autonomously from the data. You Look Only Once (YOLO) and other one-stage detectors are advantageous due to their speed and competition. This research aimed to design a model to detect powdery mildew in wheat using digital images. Multiple YOLOv8 models were trained with a custom dataset of images collected from trial areas at Tekirdag Namik Kemal University. The YOLOv8m model demonstrated the highest precision, recall, F1, and average precision values of 0.79, 0.74, 0.770, 0.76, and 0.35, respectively. Full article

Soybean pod count is a crucial aspect of soybean plant phenotyping, offering valuable reference information for breeding and planting management. Traditional manual counting methods are not only costly but also prone to errors. Existing detection-based soybean pod counting methods face challenges due to the crowded and uneven distribution of soybean pods on the plants. To tackle this issue, we propose a Soybean Pod Counting Network (SPCN) for accurate soybean pod counting. SPCN is a density map-based architecture based on Hybrid Dilated Convolution (HDC) strategy and attention mechanism for feature extraction, using the Unbalanced Optimal Transport (UOT) loss function for supervising density map generation. Additionally, we introduce a new diverse dataset, BeanCount-1500, comprising of 24,684 images of 316 soybean varieties with various backgrounds and lighting conditions. Extensive experiments on BeanCount-1500 demonstrate the advantages of SPCN in soybean pod counting with an Mean Absolute Error(MAE) and an Mean Squared Error(MSE) of 4.37 and 6.45, respectively, significantly outperforming the current competing method by a substantial margin. Its excellent performance on the Renshou2021 dataset further confirms its outstanding generalization potential. Overall, the proposed method can provide technical support for intelligent breeding and planting management of soybean, promoting the digital and precise management of agriculture in general. Full article

Antibiotic resistance genes (ARGs) are widespread in the environment, and soils, specifically, are hotspots for microorganisms with inherent antibiotic resistance. Manure and sludge used as fertilizers in agricultural production have been shown to contain vast amounts of ARGs, and due to continued applications, ARGs accumulate in agricultural soils. Some soils, however, harbor a resilience capacity that could depend on specific soil properties, as well as the presence of predatory bacteria that are able to hydrolyse living bacteria, including bacteria of clinical importance. The objectives of this study were to (i) investigate if the antibiotic resistance profile of the soil microbiota could be differently affected by the addition of cow manure, chicken manure, and sludge, and (ii) investigate if the amendments had an effect on the presence of predatory bacteria. The three organic amendments were mixed separately with a field soil, divided into pots, and incubated in a greenhouse for 28 days. Droplet digital PCR (ddPCR) was used to quantify three ARGs, two predatory bacteria, and total number of bacteria. In this study, we demonstrated that the choice of organic amendment significantly affected the antibiotic resistance profile of soil, and promoted the growth of predatory bacteria, while the total number of bacteria was unaffected. Full article

Yield prediction is an important agriculture management for crop policy making. In recent years, unmanned aerial vehicles (UAVs) and spectral sensor technology have been widely used in crop production. This study aims to evaluate the ability of UAVs equipped with spectral sensors to predict oilseed rape yield. In an experiment, RGB and hyperspectral images were captured using a UAV at the seedling (S1), budding (S2), flowering (S3), and pod (S4) stages in oilseed rape plants. Canopy reflectance and spectral indices of oilseed rape were extracted and calculated from the hyperspectral images. After correlation analysis and principal component analysis (PCA), input spectral indices were screened to build yield prediction models using random forest regression (RF), multiple linear regression (MLR), and support vector machine regression (SVM). The results showed that UAVs equipped with spectral sensors have great potential in predicting crop yield at a large scale. Machine learning approaches such as RF can improve the accuracy of yield models in comparison with traditional methods (e.g., MLR). The RF-based training model had the highest determination coefficient (R²) (0.925) and lowest relative root mean square error (RRMSE) (5.91%). In testing, the MLR-based model had the highest R² (0.732) and lowest RRMSE (11.26%). Moreover, we found that S2 was the best stage for predicting oilseed rape yield compared with the other growth stages. This study demonstrates a relatively accurate prediction for crop yield and provides valuable insight for field crop management. Full article

Mustard is an important cash crop of the genus Brassica in the family Cruciferae. Low temperature is an important environmental factor limiting the growth of mustard. In this study, 65 leafy mustard cultivars were used as experimental materials, 25 °C was set as the control temperature, and 5 °C was set as chilling stress temperature to investigated the physiological response of chlorophyll (Chl) content, soluble sugar (SS) content, proline (Pro) content, antioxidant enzyme activity, malondialdehyde (MDA) content, and chlorophyll fluorescence to chilling injury. The chilling tolerance coefficients of each individual index were measured and correlation analysis, principal component analysis (PCA), the membership function method, and cluster analysis were applied to evaluate chilling tolerance. In a comprehensive analysis, the most chilling-tolerant cultivar was SJTKJ, the least chilling-tolerant cultivar was DX. Stepwise regression was used to establish a mathematical model for evaluating the chilling tolerance of mustard, and four chilling tolerance identification indices, including Fv/Fm, ΦPSII, POD activity, and Rfd were screened. This study provides a reference for the evaluation of the chilling tolerance of mustard and the breeding of new chilling-tolerant cultivars. Full article

The connections between universal rights to water supply, energy security, and food supply stand out as a challenge that requires project evaluation models that can capture the complex dynamics and interdependencies of these resources. This study proposes the elaboration of a nexus evaluation model (NEM) for projects related to the water–energy–food nexus (WEFN) from the perspective of sustainability, Industry 4.0, and the Sustainable Development Goals (SDGs). The model considers the three dimensions of sustainability—economic, environmental, and social; the three structuring factors of Industry 4.0—physical, biological, and digital; and the 17 SDGs proposed by the United Nations. A Design Science Research (DSR) approach was adopted in which the design and development of the model, and demonstration and evaluation phases, were supported by a group of experts. The model was applied to three different projects focused on sustainable technological innovation in energy and agriculture, with the results presented in the RGB color scale represented numerically as a number from 0 to 255. The results demonstrated that, in the relationship between nexus and sustainability, the projects presented scores between 162 and 217 for the environmental dimension, between 158 and 202 for the economic dimension and between 170 and 212 for the social dimension. In the nexus and Industry 4.0 relationship, the projects obtained scores ranging from 9 to 94 in the biological factor, from 13 to 141 in the digital factor, and from 13 to 141 in the physical factor. In the nexus and SDG relationship, scores ranged from 214 to 244 for water, from 195 to 255 for energy, and from 30 to 255 for food. These results from the model were consistent with the reality of the projects being evaluated, demonstrating a greater alignment of the projects with the dimensions of sustainability and the SDGs than with the factors of Industry 4.0. The proposal of the model contributes to broaden the understanding of how projects related to the nexus can be evaluated considering multiple contemporary dimensions. Full article