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Computers and Electronics in Agriculture
Elsevier Science Publishers
Computers and Electronics in Agriculture

Elsevier Science Publishers

0168-1699

Computers and Electronics in Agriculture/Journal Computers and Electronics in AgricultureSCIEIISTP
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    Many-objective evolutionary algorithm based agricultural mobile robot route planning

    Zhang X.Guo Y.Li D.Wang Y....
    9页
    查看更多>>摘要：? 2022 Elsevier B.V.Agricultural robot technology has experienced rapid development in the past ten years, and agricultural robots have been used to implement various complex agricultural tasks. In these processes, route planning is an important guarantee for reducing navigation distance and saving total turning angle. However, minimizing the cost of the entire navigation process on the premise of completing agricultural work is difficult. Many-objective Evolutionary Algorithm is used to solve the route planning problem of agricultural mobile robots under the premise of minimizing navigation cost. By scanning the radar map of the greenhouse, the path between all target points is calculated by using the probabilistic roadmap (PRM), and the route planning of the agricultural robot is carried out according to the sum of the path length and the path angle. To determine the best route for agricultural mobile robots, four algorithms are compared: Hypervolume Estimation Algorithm (HypE), Grid-Based Evolutionary Algorithm (GrEA), Knee Point-Driven Evolutionary Algorithm (KnEA), and Non-dominated sorting genetic algorithm (NSGA-III). The quality of the solutions was compared using C-Metric, and it could verify that HypE offers the best performance among four algorithms.
      原文链接:
    • NSTL
    • Elsevier

    Teat detection of dairy cows based on deep learning neural network FS-YOLOv4 model

    Yu Z.Liu Y.Song Z.Yan Y....
    12页
    查看更多>>摘要：? 2022 Elsevier B.V.To realize automatic milking operation of large-scale dairy cow breeding, an efficient detection method for dairy cow teats, based on the improved deep learning neural network Fourth feature scale-spatial pyramid pooling-You Only Look Once v4 (FS-YOLOv4) model, was proposed to solve the problems of poor accuracy and detection slowly in the complex milking parlour in this paper. In the backbone network of the YOLOv4 model, the CSPDarknet module which had fewer parameters and low complexity was replaced by the CSResNeXt module. To improve the feature extraction of the feature map, the detection feature scale, and the Spatial Pyramid Pooling structure were added to the path augmentation network. To evaluate the performance of the FS-YOLOv4 teat detection, the test set containing 200 images was used to test the model. The results showed that the precision, recall, mAP value, and F1-score were 98.81%, 98.46%, 98.26%, and 98.63% respectively. The results were further compared with those of single-stage the YOLOv4 model, the YOLOv5 model and two-stage the Faster RCNN model. It was verified that the performance of the FS-YOLOv4 model significantly improved the detection accuracy and detection time of dairy cow's teats, and the anti-noise ability was significantly enhanced. This study provides useful exploration for automated milking.
      原文链接:
    • NSTL
    • Elsevier

    Identifying veraison process of colored wine grapes in field conditions combining deep learning and image analysis

    Song Y.Fang Y.Shen L.Chen S....
    15页
    查看更多>>摘要：? 2022Accurate identification of the veraison process is essential for improving wine quality, which is challenging due to the variability of veraison among berries of the same cluster in algorihtm design, and also the subjective and labor-intensive issues in mannual identification. Therefore, this study proposed a method combining deep learning and image analysis to identify veraison in colored wine grapes under natural field growing conditions. The removal of irrelevant background was first achieved by semantic segmentation model, and then Mask R-CNN instance segmentation pipeline was constructed with anchor parameters optimization. In particular, three kinds of backbone networks were analyzed and compared in Mask R-CNN, and the overall performance of ResNet50-FPN was the best, with the testset Average Precision reaching 81.53% and the inference time being only 45.70 ms/frame. Then, a method for characterizing berry veraison by H component of HSV color space was proposed and the invariance of the H component of three colored wine grape berries under different light conditions was verified and discussed. An algorithm was developed to identify veraison progress by calculating the percentage of the number of berries of different grades in the total number of berries of the whole grape bunches. The test accuracy reached 92.50%, 91.25% and 91.88% for three wine grapes including Cabernet Sauvignon, Matheran and Syrah respectively. The proposed method is able to provide vital reference for automated monitoring and intelligent management decisions of grape growth.
      原文链接:
    • NSTL
    • Elsevier

    Path tracking control method and performance test based on agricultural machinery pose correction

    He J.Hu L.Wang P.Liu Y....
    11页
    查看更多>>摘要：? 2022The annual rice planting area in China is approximately 30 million ha. With continuous reductions in the rural labour force and increasing production costs, it is urgent to develop unmanned technology for paddy field agricultural machinery to solve the problem of “who will farm the land”. In paddy field environments with slippery mud and uneven hard underlying, side slip and slip of paddy farm machinery can easily occur. Due to the relative movement and attitude changes between paddy farm machinery and implements and the inconsistent driving track of the farm machinery and implements, it is difficult to track and control the path of unmanned paddy farm machinery. In view of the above problems, this paper takes the paddy field agricultural machinery body as the control object and the agricultural machinery pose as the observation quantity and establishes an agricultural machinery kinematics model based on agricultural machinery pose correction. Based on the model and model predictive control (MPC), the linear model, objective function and constraint function of paddy field agricultural machinery are designed, an MPC path tracking control method is established based on the pose of the agricultural machinery, and field experiments are conducted. The results show that the average root mean square error of the three-line straight-line path tracking is 0.043 m, the average absolute error is 0.033 m, and the effect is favourable. The MPC path tracking method based on the pose correction of agricultural machinery can effectively suppress abrupt lateral position deviations caused by the relative position and attitude changes of the machine, can improve the control accuracy, and can meet the control accuracy requirements of unmanned paddy field agricultural machinery operations.
      原文链接:
    • NSTL
    • Elsevier

    Transformer helps identify kiwifruit diseases in complex natural environments

    Li X.Yang J.Li S.Chen X....
    12页
    查看更多>>摘要：? 2022 Elsevier B.V.The complex background of disease images and the small contrast between the disease area and the background easily confuse, seriously affecting the robustness and accuracy of kiwifruit disease identification models. To address the above problems, this paper proposes a disease identification model based on Vision Transformer and Convolutional Neural Network, ConvViT(Convolutional Neural Network and Vision Transformer), to identify diseases by extracting effective features of kiwifruit disease spots. The proposed ConvViT includes convolutional structure and Transformer structure: The convolutional structure is used to extract the global features of the image, and the Transformer structure is used to obtain the local features of the disease area to help the CNN see better. Meanwhile, the paper designs different models according to the number of parameters and FLOPs (floating-point operations) to improve the model's scalability. The model variants of different sizes are designed to be lightweight to run on devices with different resource constraints. We achieved 98.78% identification accuracy on the self-built kiwifruit disease dataset, with up to 4.53% improvement in identification accuracy compared to the same level of Resnet, ViT, and ResMLP, and more than 10% reduction in the number of parameters and FLOPs. Experimental results on the PlantVillage dataset and the AI Challenger 2018 also show that ConvViT has good generalizability, indicating that the proposed model can solve kiwifruit disease identification problems in complex environments and be valuable a backbone network for other identification tasks with practical applications.
      原文链接:
    • NSTL
    • Elsevier

    An automated extraction of small- and middle-sized rice fields under complex terrain based on SAR time series: A case study of Chongqing

    Wang L.Ma H.Li J.Yang Y....
    13页
    查看更多>>摘要：? 2022 The Author(s)Spatial assessment of rice cultivation area is a crucial activity, which is the foundation for the government to effectively improve the comprehensive rice production, promote the continuous increase of farmers' income and accelerate the construction of a modern rice industry system. In recent decades, large areas of arable land in Chongqing were converted to built-up land or restored to forests and grasses, resulting in a rapid decline in the rice planting area. Chongqing has an annual average of 104 cloudy and foggy days, the terrain is complex, and rice fields are generally fragmented with small- or middle-sized. How to timely obtain updated and accurate rice planting maps in Chongqing remains challenging. In this study, we processed 411 time series Sentinel-1A SAR scenes of vertical-horizontal polarization in 2020 over Chongqing, and analyzed the characteristics of rice phenological parameters under different terrain conditions, including rice transplanting date, mature grain date, length of growing season, rice agronomy flooding decline speed during sowing-transplanting period, and green-up speed during transplanting-mature period. On these bases, a decision tree algorithm integrating topographical features and rice phenological characteristics was proposed for collectively mapping patches of rice field. The identified rice results had user, producer, and overall accuracies of 0.96, 0.85 and 0.88, respectively. Meanwhile, SAR-derived rice area was compared against official rice statistical area at the county/district level with the correlation coefficient of 0.96. According to the rice map in 2020, there was a total area of 6125.70 km2 paddy rice in Chongqing, which was 6.27% lower than the data from the Chongqing statistical yearbook. Our study demonstrates the robustness and effectiveness of the proposed rice mapping method that comprehensively deliberate the topographical and rice phenological features in the complex landscapes with diverse crop types, small-medium sized and fragmented rice fields, and frequent cloudy and foggy weather.
      原文链接:
    • NSTL
    • Elsevier

    A novel underwater color correction method based on underwater imaging model and generative adversarial network

    Cai K.Yang Z.Pang H.Miao X....
    11页
    查看更多>>摘要：? 2022 Elsevier B.V.Underwater vision has played an increasingly important role in ocean research, ocean military, and underwater fishing. The raw underwater images suffer from color distortion and poor image quality, which make underwater vision more challengeable than open air vision. To address the mentioned problems, a novel model for underwater image color correction is proposed in this paper. Firstly, a synthetic underwater image generating network is presented to overcome the lack of effective underwater training data. Specifically, it combines a background color prior model to generate synthetic underwater images via in-air images data. By the benefit of the color prior and imaging algorithm, the generating model can be with fewer number of parameters and more effective. Meanwhile, a simple yet effective model is proposed to train on the in-air images and corresponding rendered synthetic underwater images for color correction. Different from other image restoration models with multiple substructure or complicated construction, the proposed method has only few convolution layers and combining with a dense-like and a res-like structure. Finally, the enhanced results demonstrate the superiority of the proposed method, which performs favorably against the existing state of the art methods in both effectiveness and model size aspects.
      原文链接:
    • NSTL
    • Elsevier

    Field-scale estimation of sugarcane leaf nitrogen content using vegetation indices and spectral bands of Sentinel-2: Application of random forest and support vector regression

    Minaei S.Soltanikazemi M.Mahdavian A.Shafizadeh-Moghadam H....
    9页
    查看更多>>摘要：? 2022 Elsevier B.V.Nitrogen is an essential factor for assessing the quality of sugarcane during the growing season, as deficiency of this component significantly reduces crop yield. The Kjeldahl method is the most common approach to measuring sugarcane nitrogen, but it is a laborious, costly, and time-consuming process. Conversely, multispectral satellite imagery can provide timely, cost-effective, and large-scale information on the nitrogen content in sugarcane fields. The current study applied random forest (RF) and support vector regression (SVR) models to estimate sugarcane leaf nitrogen using vegetation indices and spectral bands of Sentinel-2. In-situ data was taken from 45 farms (1125 ha) in a sugarcane production agro-industrial complex in southwest Iran. The global environmental monitoring index (GEMI), chlorophyll index green (Clgreen), and Sentinel-2 red-edge position index (S2REP) were found to be the most important variables related to sugarcane nitrogen. The coefficient of determination (R2) for RF and SVR was 0.59 and 0.58, respectively, and the corresponding root mean square error (RMSE) was 0.08 and 0.09, respectively. Despite the similar performances of the two models, RF showed higher accuracy; however, to improve the results, the use of multi-temporal data for model calibration is recommended.
      原文链接:
    • NSTL
    • Elsevier

    Applying convolutional neural networks for detecting wheat stripe rust transmission centers under complex field conditions using RGB-based high spatial resolution images from UAVs

    Deng J.Zhou H.Lv X.Yang L....
    13页
    查看更多>>摘要：? 2022 Elsevier B.V.The use of unmanned aerial vehicle (UAV) provide a timely and low-cost means of accessing high spatial resolution imagery for crop disease detection. In this study, convolutional neural networks (CNNs) and RGB-based high spatial resolution images from UAVs were explored to detect wheat stripe rust transmission centers (Infected area accounted less than 1.35 %) occurrence in complex fields conditions in Hubei, China. To take full advantage of end-to-end learning capabilities, CNNs semantic segmentation architecture (deeplabv3+) was applied to per-pixel classify the imagery for the detection of healthy wheat and stripe-rust-infected wheat (SRIW). Using a rich dataset with diverse field conditions and sunlight illumination properties, we were able to accurately detect SRIW (Rust class F1 = 0.81). The study also evaluated the impact of classification framework and spatial resolution on model training. It revealed that the model accuracies improved for the rust class when the multi-branching binary framework instead of the multi-classification framework for CNN training with unbalanced classes. A coarser spatial resolution (8 cm) significantly decreased the model accuracy (Rust class F1-score). In addition, the Macro-disease index(MDI) was defined to quantitatively measure the occurrence of SRIW. Our results demonstrate the capability of ultra-high spatial resolution UAV imaging in detecting SRIW. With the end-to-end deep learning segmentation method greatly reducing the need for intensive preprocessing, the combination of CNNs and RGB-based ultra-high spatial resolution images from UAVs provides a simple and rapid method for accurate detection of crop disease on a large scale.
      原文链接:
    • NSTL
    • Elsevier

    Modeling response of spring wheat yield to soil water and salt contents and its application in scheduling brackish water irrigation

    Chen S.Song C.Shang S.Mao X....
    11页
    查看更多>>摘要：? 2022 Elsevier B.V.Fresh water shortage and soil salinization are two key factors restricting agricultural development in arid areas. Brackish water is a possible alternative resource for crop irrigation, but brackish water irrigation should be properly scheduled to balance crop yield increment and soil salinization control. To determine suitable water amounts for crop irrigation under different water salinities, an agro-hydrological model, LAWSTAC, was used to simulate soil water and salt dynamics and crop growth in salinized croplands in an arid region of Northwest China. Model calibration and validation results of soil water content, salt concentration, groundwater table depth, leaf area index, biomass, and crop yield agreed well with the field measurements of spring wheat (Triticum aestivum L.) under both fresh and brackish water irrigations with the Nash and Sutcliffe efficiency of 0.50–0.96 and the coefficient of determination of 0.55–0.98. The model was further used to simulate crop yields for 432 combinations of six irrigation levels, six irrigation water salinity levels, four initial groundwater table depths, and three initial soil salt concentrations. Based on the simulation results, a function of relative crop yield response to soil water content and salt concentration was developed, which is expressed as the product of a hyperbolic function for water stress and a sigmoid function for salt stress. For known initial soil water content and salt concentration, a procedure was proposed to determine suitable water amounts applied to the cropland under different irrigation water salinities for expected crop yields using the developed crop-water-salinity production function. With the procedure, the minimum amount of applied water with known salinity can be obtained for an expected relative crop yield. The results are helpful to the amount control in fresh or brackish water irrigations for sustainable crop production in salinized croplands.
      原文链接:
    • NSTL
    • Elsevier