Computers and Electronics in Agriculture最新文献

In recent decades, extensive research has focused on estimating winter wheat yields and developing methods for collecting the necessary field data. However, it may be advantageous to first evaluate which data types and levels of model complexity are truly essential. This study examined the explanatory power of water regime modeling, the green area index (GAI), site-specific soil texture, and harvest index (HI) data in estimating winter wheat yields throughout the growing season. Data collected over 13 years from measurements of GAI and soil moisture in winter wheat plot trials in northern Germany were integrated into a plant growth and soil water budget model (HUME). The soil moisture data were used to estimate site-specific soil textures. Monitoring GAI was identified as the key factor for explaining yield. The increased modeling effort of integrating GAI into HUME was found to be justified. The modelled transpiration provided a more accurate explanation of the yield at the end of the season (R² = 0.86) compared to radiation uptake (R² = 0.73). Additionally, predictors based on transpiration were less dependent on GAI senescence and HI data. By the time of the third N fertilization, the most effective predictor tested − transpiration standardized daily by the saturation deficit of the air – allowed for the prediction of a substantial portion of site- and year-specific grain yield variation (R² = 0.64). This suggests that it could serve as a valuable starting point for developing N management strategies. Site-specific soil textures only marginally improved yield estimation, with an increase in R² of less than 0.05. The findings indicate that interpreting GAI through soil water modeling can uncover water limitations that affect winter wheat yield, even in temperate climates. This underscored the importance of ongoing research to generate comprehensive, site-specific GAI data throughout the growing season. Alongside the results and methodological approach discussed here, such data could potentially enable nitrogen fertilization management driven by yield predictions in the future, thereby improving N efficiency in wheat cultivation.

Quail egg size varies due to differences in age, nutrition, and genotype of birds. The grading of eggs is significant, particularly for quail breeding farms, as it facilitates pairing similar phenotypic features for selecting optimal genetic strains. However, this process currently requires skilled manpower and complex equipment. In this work, we introduce a new system to grade quail eggs according to their size using a deep learning-based machine vision approach. The prototype was able to classify and segregate eggs into up to 4 classes: small, medium, large, and extra large. The new system was divided into two modules: the classification module and the sorting module. In the first module, each egg was tracked by the deep SORT algorithm and graded proportionally to the bounding box sizes using the deep learning-based computer vision models; in the second module, a pneumatic system was developed along with a damage prevention designed conveyor belt for sorting the egg classes. The classification accuracy rate found from comparing real classes and machine vision predictions showed that 4 or 3 classes were reliable in sorting out egg sizes with higher confidence, proving to be useful at a feasible implementation cost to support farmers. Three object detection algorithms were compared: EfficientDet, CenterNet, and YOLOv7. The models were scaled into 2 network sizes, 512 × 512 and 1024 × 1024, and the results were compared in terms of egg class prediction accuracy and real-time inference speed. In terms of class prediction accuracy, the best results for grading quail eggs into 4 classes were achieved by the EfficientDet-1024, CenterNet-512, and YOLOv7-1024 models, which correctly classified 78 %, 77.5 % and 72 %, respectively. While classifying the eggs into 3 classes, again, the best model was observed from EfficientDet-1024 (86 %), followed by CenterNet-512 (84 %) and YOLOv7-1024 (80 %). Regarding the trade-off between real-time inference speed and grading accuracy, YOLOv7 outperformed the compared models by inferencing at 40.38 % faster than CenterNet-512, which was the second fastest and most accurate grader. As result, the best compared model was estimated to grade at pace of 797 quail eggs per hour. The proposed combined machine vision-based system can be further scaled up either in small-scale farming or industrial anticipation for consumer satisfaction and to ensure safe production traceability.

Context

Grey leaf spot is a main leaf disease of tomato in Mediterranean greenhouses, characterized by warm temperatures and high humidity during the spring and winter seasons, hence suitable for pathogen infection and spore spread. Consequently, the utilization of automatic control and optimization algorithms has emerged as effective means to prevent chemical-oriented disease control and enhancing the overall quality and safety of food and crops.

Objective

The aim of this work is to search an optimal strategy for precision management on greenhouse tomato growth environment. So, multi-objective optimization rises to an alternative to achieve this goal. While there were lots of research on determining trajectories to control a desired crop growth, and lacking works that optimize climate conditions for restraining the damage of disease on crop.

Methods

Based on the multi-objective genetic algorithm optimization method (MOGA), the solution balances the conflict of two objectives: minimum power cost caused by climate control and maximum health leaves with few effects of grey leaf spot. This study also highlights disease and high temperature impact on tomato growth, which are as inequality constraints of the optimization problems.

Results and conclusions

The results showed MOGA strategy performers good, the minimum power cost is only need 0.084€*day⁻¹ in warm weather condition, as well as 3.74 €*day⁻¹ in cold weather condition, the uninfected LAI (m²[Leaves](m⁻²[soils]*day⁻¹)) is the range of [0.14 0.20]. The yearly power cost at least [308 1365]€.These are able to embed within a control scheme for achieving optimization purpose.

Significance

The farmer receives the data necessary for decision-making to establish the setpoints during the crop cycle, modifying the control decisions, lowering production costs, reducing the use of pesticides and increasing the system efficiency to optimize crop growth.

Agroforestry crops such as apples, peaches and pears are horticultural crops, which are an important part of modern agriculture and are of great economic and social importance. Accurate crop data at large scales (e.g., regional) are critical for effective agricultural management and resource regulation. However, existing orchard statistics, survey data, and expert knowledge are often lagging and of low confidence, lacking detailed data on the spatial distribution of orchards. The sparse distribution and indefinite characteristics of orchards compared to field crops, as well as the large intra-class variance of fruit tree spectra, make large-scale mapping of orchards a major limitation and huge challenge. To address these challenges, we developed an orchard mapping index (OMI) based on the phenology and green-holding characteristics of fruit trees, and automated orchard mapping algorithm using sentinel-2 time-series imagery and the Google Earth Engine platform (GEE). Fruit trees have unique phenological and greening characteristics: fruit tree canopies turn green earlier, turn yellow later, and have a long greenness saturation time in annual growth cycles. The proposed OMI index significantly captures the difference in green-holding between orchards and non-orchards [1.5*Interquartile Range (IQR): 0.72–39.5 for orchards, 0.10–3.36 for non-orchards]. The mapping algorithm successfully mapped 10 m-resolution orchard maps in the Loess Plateau region of China from 2020 to 2022, with an overall accuracy of 89.95–93.51 % and a kappa of 0.80–0.87. We have additionally identified that the implementation of a fine-grained agricultural plantation zoning mapping strategy exhibits the potential to enhance the performance of orchard mapping. Our study demonstrated the potential of a phenology-based approach, sentinel image data, and the GEE platform for orchard mapping, and for the first time developed a large-scale map of orchards in the Loess Plateau region of China. This study not only fills the gap of large-scale orchard mapping algorithm and products but also provides valuable spatial information for fruit tree flowering prediction, disease prevention and yield prediction.

How to further improve the working performance of combine harvester is increasingly focused. Because the harvest time of crops in agricultural production is generally short, the number of field trials of harvesters is limited, which severely hindered the study of combine harvesters. The digital twin system can conduct a large number of simulation tests on the harvester in a virtual environment, and it is not limited by operation time and operation scenarios, which has obvious advantages. Addressing the issue that current digital twin systems for agricultural machinery rely heavily on large-scale physical engines and the combine harvester digital twin system lacks a method for modeling multi-component complex transmissions; this study introduces a lightweight network-based approach to construct a digital twin system for combine harvester, encompassing multiple subsystems such as physical, virtual, model calculation, data interaction, and human–computer interaction. Among them, it studies the complex transmission and motion pattern classification of critical components of combine harvester, proposing a method for modeling the kinematic relationships of complex motions in critical components, accurately modeling different types of physical activities provides crucial technical support for the precise mapping of digital twin systems. Ultimately, using the Lovol GM100 combine harvester as a case study and leveraging the CMOnlineLib and HTML lightweight network, a digital twin system was developed for the combine harvester, including creating a LightGBM model capable of predicting fuel consumption. Field tests demonstrate that the digital twin system for the combine harvester operates stably and reliably, with the fuel consumption prediction model under full-load conditions achieving an average error of 0.24 L/h, a maximum error of 0.84 L/h, and an average relative error of only 1.09 %. This research offers a novel approach to enhancing the digital twin technology and increasing the intelligence level of combine harvester.

Agriculture faces a major challenge in meeting the world’s growing demand for food in a sustainable manner in the face of increasing environmental pressures, in particular the growing impact of climate change. Agriculture is also a major contributor to climate change. Digital technologies in agriculture can contribute to climate change adaptation and mitigation. This paper examines the interactions between climate change and agriculture, reviews adaptation and mitigation strategies, explores the application of digital technologies in this context, and discusses future challenges and opportunities for sustainable and resilient agriculture. The final aim is to provide a comprehensive overview of the current state and future prospects of digital agriculture in the context of climate change. A comprehensive literature review was conducted on adaptation and mitigation strategies in agriculture, and on the current state and future prospects of digital agriculture in the context of climate change adaptation and mitigation. The identified applications of digital technologies in agriculture include Remote Sensing for crop monitoring, Big Data for predictive modelling of water shortages and pest outbreaks, Artificial Intelligence for pest identification and tracking, the Internet of Things for precision fertiliser management, nanotechnology for soil improvement, robots for targeted spraying, and blockchain for improved soil management and supply chain transparency, among others. These technologies facilitate the precise management of resources, improve decision-making processes and enable more efficient agricultural practices. Digital technologies also help mitigate climate change by optimising inputs such as water and fertiliser, thereby reducing greenhouse gas emissions and promoting carbon sequestration. However, there are significant barriers to the adoption of these technologies, including the digital divide, high up-front costs and complexity, as well as privacy and security concerns and the environmental impact of technology use. Future action must address these barriers by investing in infrastructure and training, ensuring financial incentives, developing scalable digital solutions tailored to local agricultural conditions, increasing digital literacy among farmers, developing comprehensive governance frameworks, and exploring the integration of multiple digital technologies. The paper contributes to advancing scientific understanding and guiding practice and policy towards sustainable agriculture in the face of climate change. It provides a call to action for a more sustainable future in the context of climate change and highlights the urgency of multi-stakeholder collaboration to create an enabling environment for the widespread adoption of these innovations, ensuring that they are accessible, cost-effective and suitable for different farming environments.

This work investigates the friction generated by different materials (maize kernels, pinewood pellets and plastic pellets) on corrugated steel silo walls and examines the validity of the wall contact factor defined in Eurocode EN 1991–4 for estimating the frictional force on the vertical wall. A model silo was constructed with corrugated steel lateral walls, methacrylate front and back walls, and with a configurable base to set different discharge conditions. Normal and tangential forces at different heights on the silo walls were recorded, along with the mass flow index, to determine whether the flow of the material during discharge was greater with decreasing material particle size. An increase in normal and tangential forces was recorded for all materials tested during discharge, especially in the upper part of the silo. Pinewood pellets showed a greater mobilisation of internal friction as compared to plastic pellets and maize kernels.

Alfalfa, a high-quality forage, has good palatability and nutritional value. Neutral detergent fiber (NDF) and acid detergent fiber (ADF) are both key indicators of alfalfa quality. However, the uncertainties in existing studies regarding the sensitive bands and inversion mechanism for NDF and ADF contents estimations have limited the application of high-precision remote sensing-based inversion. In this study, using hyperspectral and Sentinel-2 (S2) multispectral data of cultivated alfalfa in the Hexi Corridor region from 2020 to 2022, we analyze the characteristic spectral band and vegetation indices (VIs) required to estimate the NDF and ADF contents of alfalfa. The key conclusions are as follows. (1) The sensitive bands selected using ASD hyperspectral data are mainly in the blue, green, red-edge, and short-wave infrared (SWIR) regions, while the sensitive bands based on S2 data cover a broader range between the blue and SWIR regions. (2) Among the 21 NDF and 21 ADF models based on ASD data in this study, the optimal models are both artificial neural network (ANN) models constructed by VIs (R² of 0.80 for both, RMSEs of 2.27% and 1.75% and mean absolute errors (MAEs) of 1.77% and 1.38% for NDF and ADF, respectively). For the S2 data, the optimal models are also ANN-based and constructed using VIs (with R² values of 0.66 and 0.72, RMSEs of 3.06% and 2.24%, and MAEs of 2.50% and 1.79% for NDF and ADF, respectively. (3) The inversion results using the optimal model indicate that the proportion of alfalfa area in the typical study area with NDF and ADF contents characterized by a supreme grade is greater than 60%. Overall, both the ASD hyperspectral and S2 multispectral data can accurately predict alfalfa NDF and ADF contents. This approach provides an effective technical means by which the management of local alfalfa production may be guided.

Poultry farming plays a pivotal role in addressing human food demand. Robots are emerging as promising tools in poultry farming, with the potential to address sustainability issues while meeting the increasing production needs and demand for animal welfare. This review presents current advancements and limitations in robotics in poultry farming, and outlines future directions of development including robot-animal interactions. We survey the application of robots in different areas, from environmental monitoring to disease control, floor eggs collection and animal welfare. Robots not only demonstrate effective implementation on farms but also hold potential for ethological research on collective and social behaviour, which can in turn drive a better integration in industrial farming, with improved productivity and enhanced animal welfare.

In this study, we propose to utilize the canopy features such as canopy cover (CC), canopy height (CH), canopy volume (CV), and excess greenness index (ExG) extracted from UAVs imagery and Bayesian neural network (BNN) to develop a pipeline for predicting cotton crop yield. The pipeline consisted of two components, data imputation which dealt with irregular spatial and temporal data and yielded prediction with uncertainty quantification. The data was collected from producers’ fields in 2020, 2021, and 2022. To assess the performance of the proposed BNN model particularly for the generalization across years, three other models including support vector regression (SVR), random forest regression (RFR), and multiple layer perceptron (ML) were used. In cross year test, our pipeline produced better results with root mean squared error (RMSE) of 365.22 kg ha⁻¹, mean absolute error (MAE) of 294.5 kg ha⁻¹, and R² of 0.67 between actual yield and the yield prediction by the model. In addition, feature importance analysis showed that the combination of CC, CH, CV, and ExG followed by the combinaton of CC and ExG variables outperformed other combinations or single variables.