首页期刊导航|Computers and Electronics in Agriculture
期刊信息/Journal information
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
正式出版
收录年代

    Predicting and interpreting cotton yield and its determinants under long-term conservation management practices using machine learning

    Kaur Dhaliwal J.Panday D.Saha D.Lee J....
    12页
    查看更多>>摘要：? 2022 Elsevier B.V.Accurate predictions of crop yield are an integral part of effective agricultural tactical and strategic management decisions to sustain yield without adversely impacting the environment. Process-based simulation models are widely used to predict crop yields, but their application remains limited by the requirements of substantial expertise, intensive data, and extensive calibration. Therefore, greater attention is currently being devoted to machine learning (ML) methods that are more computationally expedient. We evaluated six ML algorithms (linear regression, ridge regression, lasso regression, random forest, XGboost, and artificial neural network (ANN)) to predict cotton (Gossypium spp.) yield and determine the yield response to critical determinants using long-term (1986–2018) data on management, climate, historical yield, and point measurement of soil organic carbon (SOC) from continuous no-till (NT) cotton cropping system in west Tennessee. Data from 1986 to 2015 were used for model training, hyper-parameterization and testing, while data from 2016 to 2018 were used for independent model validation. Results showed that tree-based models (random forest and XGboost) outperformed other models in predicting lint yield. The variable importance scores, predicted by random forest model, indicated that cotton yield was more responsive to management variables (nitrogen (N) fertilization rate, cover crop, and years since NT establishment) followed by soil (SOC) and climate variables at the study site. The model identified optimal N application at 60 kg ha-1N rate for cotton lint yield, and also highlighted that the benefits of NT in enhancing yield can be achieved after 15 years of practice. The model predicted that the long-term adoption of hairy vetch (Vicia villosa), a legume cover crop, has the potential to increase cotton yield. Among the climate variables, cotton lint yield was most impacted by average maximum temperature and precipitation during flowering to open boll period. While random forest and XGboost proved to be the most effective ML models in this study, multi-site data is needed to build a more robust model with greater generalization and interpretation capabilities under a wider prediction domain.
      原文链接:
    • NSTL
    • Elsevier

    Development and assessment of a novel camera-integrated spraying needle nozzle design for targeted micro-dose spraying in precision weed control

    Ozluoymak O.B.
    10页
    查看更多>>摘要：? 2022 Elsevier B.V.While pesticide use is very important for weed control in agriculture, it is critical due to environmental contamination. In this study, a novel camera-integrated spraying needle nozzle design for targeted micro-dose spraying in precision weed control was developed in order to avoid excessive pesticide use. The micro-dose spraying system consisted of a camera, two pan-tilt units with servomotors assembled together to provide a 360-degree spraying capability for artificial weed samples, and spraying equipment. All the system automation and image processing processes were evaluated and controlled using LabVIEW software. The shooting capability and spraying performance of the novel camera-integrated spraying needle nozzle were tested and evaluated under laboratory conditions using artificial weed samples placed on a conveyor belt. The greenness method was used to detect the artificial weed targets on the conveyor belt. After image capturing, the coordinates of all artificial weeds in the field of view were calculated, and micro-dose spraying was then carried out for each artificial weed sample one by one until all the samples were sprayed. Positional error tests were carried out to assess the targeting performance of the spraying system. Deposition experiments were also carried out using filter papers to evaluate the spraying efficiency of the micro-dose spraying system under 200 kPa spraying pressure. A conveyor belt was set up for carrying the filter papers. The positional error test results showed that the weeding mean positional errors were 7.72 mm, 22.40 mm and 23.34 mm for the centre, left and right sides of the conveyor belt, respectively. The deposition concentration results showed that, while the mean deposition was 1.923 ng ml?1 for the centre, the mean depositions were 1.567 ng ml?1 and 1.494 ng ml?1 for left and right sides, respectively. Higher spraying efficiency was determined in the centre compared to the left and right sides according to the amount of deposition results. The novel micro-dose spraying system has been experimentally tested and found to be very efficient.
      原文链接:
    • NSTL
    • Elsevier

    A deep learning approach incorporating YOLO v5 and attention mechanisms for field real-time detection of the invasive weed Solanum rostratum Dunal seedlings

    Cheng M.Cai Z.Wang Q.Yuan H....
    11页
    查看更多>>摘要：? 2022 Elsevier B.V.Solanum rostratum Dunal is a common invasive alien weed that can damage native ecosystems and biodiversity. Detecting Solanum rostratum Dunal at an early stage of growth will make it possible to treat it before it causes serious damage. Therefore, a convolution neural network model YOLO-CBAM is constructed in this paper for the detection of the Solanum rostratum Dunal seedings, which is incorporating YOLO v5 and attention mechanism. A method is designed for slicing the high-resolution images by calculating the overlap rate to construct datasets that reduce the possibility of detail loss due to compressing high-resolution images during the training process. Multiscale training methods have been used to improve training performance. The comparison tests show that the Precision and Recall of the proposed YOLO_CBAM are both higher than that of YOLO v5. The performance of the network is further improved after multi-scale training, and the Average Precision (AP) of YOLO_CBAM increased from 0.9017 to 0.9272. The trained network model was deployed to Jetson AGX Xavier for field trials. The network model achieved a Precision of 0.9465 and a Recall of 0.9017 for real-time recognition. The detection speed and the detection effectiveness can be applied to field real-time detection of the invasive weed Solanum rostratum Dunal seedlings.
      原文链接:
    • NSTL
    • Elsevier

    Agricultural Innovization: An Optimization-Driven solution for sustainable agricultural intensification in Michigan

    Kropp I.Nejadhashemi A.P.Hernandez-Suarez J.S.Jha P....
    12页
    查看更多>>摘要：? 2022 Elsevier B.V.Humanity, now increasingly populous and affluent, poses a new challenge for the twentieth-first century farmer: increase food supply while maintaining the earth's underlying ecosystems. This paper proposes a novel systems approach, agricultural innovization, to sustainably increase food production. Innovization applies the knowledge obtained through multi-objective optimization to discover new agricultural management practices which reduce the risk of climate variabilities on crop yields. In agricultural innovization, an optimization platform generated the near-optimal management actions for 30 years using a calibrated crop model for maize. From those near-optimal solutions, recommendations for improving management practices were data mined. Then these improved recommended practices were evaluated over 420 validation seasons. The validation results were promising as the recommended practices obtained from the innovization increased yields and generated no negative change in nitrogen leaching. Furthermore, these recommendations can be applied to future seasons of management, which makes them a fully predictive application of multi-objective optimization.
      原文链接:
    • NSTL
    • Elsevier

    Extraction of key regions of beef cattle based on bidirectional tomographic slice features from point cloud data

    Li J.Ma W.Zhao C.Li Q....
    13页
    查看更多>>摘要：? 2022 Elsevier B.V.As the world population grows and the increase in food consumption creates unprecedented pressure on beef producers, innovative technologies for sustainable and efficient beef production is quintessential. Therefore, a significant effort is ongoing on developing digital precision technologies for continuous beef cattle monitoring and accurate cattle growth estimation. One such technology relies on 3D visual sensor technologies. 3D point cloud data acquired with 3D cameras can be processed to evaluate the livestock's growth condition with minimal human intervention. Essentially, the automatic measurement of animal body dimensions highly depends on the location accuracy of the key points or regions in point clouds. However, the location of specific points and regions requires extra assistance and usually leads to semi-automatic and low-accuracy solutions at best. To address these issues, we demonstrated a novel strategy for partial segmentation of beef cattle point clouds, based on the Bidirectional Tomographic Slice Segmentation (BTSS) algorithm. The cattle point clouds can be successfully segmented into the head and neck, front trunk, middle trunk, back trunk, lower leg, and hip and tail regions with high accuracies of 89%, 91%, 94%, 95%, 92%, and 95%, respectively. Consequently, the segmentation completion rate is 96%, and the average segmentation accuracy reaches 92.8%. When compared to traditional approaches, our method efficiently extracted abundant body characteristics from the beef cattle point cloud. Moreover, the proposed algorithm exhibited generalization capabilities to segment the point clouds of other cloven-hoofed livestock species. The accurate localization of key regions enabled body dimensions measurements and non-contact weighing, which can provide a solid support for breeding needs such as health evaluation, production performance measurement, and genetic breeding evaluation.
      原文链接:
    • NSTL
    • Elsevier

    MS-DNet: A mobile neural network for plant disease identification

    Chen W.Chen J.Duan R.Fang Y....
    11页
    查看更多>>摘要：? 2022 Elsevier B.V.Plant disease identification has recently attracted immense attention from the perspective of food security. Owing to the complexity and diversity of plant diseases, plant disease recognition using image processing techniques is a challenging task. Although the widely applied deep neural networks are promising for recognizing diverse plant diseases, they have certain drawbacks such as their requirement for a large number of parameters, which necessitates a large amount of annotation data for training models. To overcome this challenge, this study proposes a novel lightweight network architecture called MS-DNet for the recognition of crop diseases; the network has a small model size and high computation speed. The proposed method has attained a satisfactory performance in comparative experiments, with the highest average accuracy of 98.32% in recognizing different crop disease types. The experimental results further show that the proposed method outperforms other state-of-the-art methods and also demonstrate its efficiency and extensibility. Our code is available at https://github.com/xtu502/Automatic-crop-disease-identification-under-field-conditions.
      原文链接:
    • NSTL
    • Elsevier

    Gas chromatography-ion mobility spectrometric discrimination of trunk borer infested Platycladus orientalis using a novel topographic segmentation strategy

    Zheng C.Zhou Q.Kang S.Zhang J....
    9页
    查看更多>>摘要：? 2022 Elsevier B.V.Gas chromatography coupled with ion mobility spectrometry (GC-IMS), a volatile analysis technique, has been widely used in the agricultural field over the past decade. However, complex three-dimensional (3D) fingerprint, including millions of data elements, causes significant challenges for its data process. This study proposed a novel topographic segmentation-based strategy, which could automatically extract the maxima and volume features from individual peaks. Compared with the manual marker selection approach, its efficiency was identified using different classification and prediction models based on trunk borer infested Platycladus orientalis samples. The grid search-support vector machine (GS-SVM) classifier combined with a topographic segmentation strategy could correctly classify at least 93.67% of P. orientalis samples into their corresponding infestation stages. The volume feature performed best, and its cross-validation classification accuracy could reach 97.05%. The PLSR prediction model based on the volume feature got the most satisfactory result, whose Rc2 = 0.9624 and RMSEC = 6.328 in calibration set and Rp2 = 0.9056 and RMSEP = 9.926 in validation set. In a word, our proposed topographic segmentation strategy had enough capability to extract local maxima and volume features of peaks from GC-IMS fingerprints. The GC-IMS-based approach combined with chemometric methods had the potential to discriminate the infestation stages of trunk borer damaged P. orientalis plants.
      原文链接:
    • NSTL
    • Elsevier

    Design and evaluation of hedge trimmer robot

    Massah J.Jamzad M.Kamandar M.R.
    17页
    查看更多>>摘要：? 2022 Elsevier B.V.An autonomous hedge trimmer robot was developed to reduce the harmful effects of traditional hedge trimming method (gasoline-powered hedge trimmer) on operators' bodies and increase the speed and quality of the operation. The robot had five degrees of freedom (PRRRR) structure and was geometrically optimized to simplify the control strategy, enabling it to trim simple, stepped, and circular forms. The control system, consisting of a host computer, servo motors and servo drivers, and controller, guided the robot platform, manipulator, and end-effector as it approached and trimmed the hedge. By using a recognition algorithm and vision-based navigation system, the robot was able to detect the path and trim the hedge automatically. Experimental tests showed the vision-based system is accurate and fast enough to control the robot. The accuracy of hedge detection performance under various conditions was about 96% and the success rate of trimming was 88%.
      原文链接:
    • NSTL
    • Elsevier