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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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    Improving wheat yield estimates using data augmentation models and remotely sensed biophysical indices within deep neural networks in the Guanzhong Plain, PR China

    Zhang J.Tian H.Wang P.Tansey K....
    13页
    查看更多>>摘要：? 2021 The AuthorsCrop yield estimation and prediction constitutes a key issue in agricultural management, particularly under the context of demographic pressure and climate change. Currently, the main challenge in estimating crop yields based on remotely sensed data and data-driven methods is how to cope with small datasets and the limited amount of annotated samples. In order to cope with small datasets and the limited amount of annotated samples and improve the accuracy of winter wheat yield estimation in the Guanzhong Plain, PR China, this study proposed a method of combining generative adversarial networks (GANs) and convolutional neural network (CNN) for comprehensive growth monitoring of winter wheat, in which the remotely sensed leaf area index (LAI), vegetation temperature condition index (VTCI) and meteorological data at four growth stages of winter wheat during 2012–2017 were generated as the inputs of multi-layer convolutional neural networks (CNNs), and GAN was employed to artificially increase the number of training samples. Then, a linear regression model between the simulated comprehensive growth monitoring (I) and the measured yields was established to estimate yields of winter wheat in the Guanzhong Plain pixel by pixel. The final results showed when GAN was used to double the size of the training samples, and the simulation values obtained by CNN based on augmented samples using GAN provided a better training (R2 = 0.95, RMSE = 0.05), validation (R2 = 0.54, RMSE = 0.16) and testing (R2 = 0.50, RMSE = 0.14) performance than that just using the original samples. The achieved best pixel-scale yield estimation accuracy of winter wheat (R2 = 0.50, RMSE = 591.46 kg/ha) in the Guanzhong Plain. These results showed that small samples can be enlarged by GAN, thus, more important features for reflecting the growth conditions and yields of winter wheat from the remotely sensed indices and meteorological indices can be extracted, and indicated that CNN accompanied with GAN could contribute a lot to the comprehensive growth monitoring and yield estimation of winter wheat and data augmentation methods are extremely useful for the application of small samples in deep learning.
      原文链接:
    • NSTL
    • Elsevier

    Optimal smart contract for autonomous greenhouse environment based on IoT blockchain network in agriculture

    Jamil F.Ibrahim M.Ullah I.Kim D.-H....
    18页
    查看更多>>摘要：? 2021The Internet of Things (IoT) has been widely adopted in many smart applications such as smart cities, healthcare, smart farms, industry etc. In recent few years, the greenhouse industry has earned significant consideration from the agriculture community due to its ability to produce fresh agricultural products with immense growth and production rate. However, labour and energy consumption costs increase the production cost of the greenhouse by 40–50% approximately. Moreover, the security and authenticity of agriculture data, particularly for yield monitoring and analysis, is also a challenging issue in current greenhouse systems.The greenhouse require optimal parameter settings with controlled environment to produce increase food production. Therefore, slight advancement can bring remarkable improvements concerning the increase in production with reduced overall cost. In this work, we contributed blockchain enabled optimization approach for greenhouse system. The proposed approach works in three steps to provide optimal greenhouse environment that are; prediction, optimization, and finally controlling. Initially, the Kalman filter algorithm is employed for predicting the greenhouse sensor data. In next step, the optimal parameters are computed for the indoor greenhouse environment. Finally, the optimized parameters are utilized by the control module to operate and regulate the actuator's state to meet the desired settings in the indoor environment. To evaluate the performance of our proposed greenhouse system, we have developed an emulation tool. The proposed system has been investigated and compared against baseline approach concerning production rate and energy consumption. The obtained results reveal that the proposed optimization approach has improved the energy consumption by 19% against the prediction based approach and 41% against the Baseline scheme. Furthermore, the proof-of-concept based on the Hyperledger Fabric network is implemented on the top of the proposed greenhouse platform. For experimental analysis, we have conducted a series of experiments using Hyperledger calliper concerning throughput, latency, and resource utilization. These results advocates the efficiency of the proposed optimal greenhouse system.
      原文链接:
    • NSTL
    • Elsevier

    A robust computational approach for jaw movement detection and classification in grazing cattle using acoustic signals

    Martinez-Rau L.S.Chelotti J.O.Vanrell S.R.Rufiner H.L....
    13页
    查看更多>>摘要：? 2021 Elsevier B.V.Monitoring behaviour of the grazing livestock is a difficult task because of its demanding requirements (continuous operation, large amount of information, computational efficiency, device portability, precision and accuracy) under harsh environmental conditions. Detection and classification of jaw movements (JM) events are essential for estimating information related with foraging behaviour. Acoustic monitoring is the best way to classify and quantify ruminant events related with its foraging behaviour. Although existing acoustic methods are computationally efficient, a common failure for broad applications is the deal with interference associated with environmental noises. In this work, the acoustic method, called Chew-Bite Energy Based Algorithm (CBEBA), is proposed to automatically detect and classify masticatory events of grazing cattle. The system incorporates computations of instantaneous power signal for JM-events classification associated with chews, bites and composite chew-bites, and additionally between two classes of chew events: i) low energy chews that are associated with rumination and ii) high energy chews that are associated with grazing. The results demonstrate that CBEBA achieve a recognition rate of 91.9% and 91.6% in noiseless and noisy conditions, respectively, with a high classification precision and a marginal increment of computational cost compared to previous algorithms, suggesting feasibility for implementation in low-cost embedded systems.
      原文链接:
    • NSTL
    • Elsevier

    Retinex-inspired color correction and detail preserved fusion for underwater image enhancement

    Zhang W.Dong L.Xu W.
    14页
    查看更多>>摘要：? 2021 Elsevier B.V.Many practical underwater applications need to address color cast, blurring, and low contrast of underwater images for display and further analysis. To cope with the aforementioned issues, a novel underwater image enhancement method based on Retinex-inspired color correction and detail preserved fusion technology is proposed. First, we employ a Retinex-inspired color correction strategy to remove the color cast induced by underwater light scattering. We then build on blending three images directly derived from a local contrast-enhanced version, a detail version, and a global contrast-enhanced version of the corrected underwater image. The local and global contrast-enhanced versions promote the transfer of color and contrast to the output image. Meanwhile, the detail version improves the edges and details of the output image. Qualitative and quantitative comparisons demonstrated that the proposed method achieves superior enhancement performance for underwater images of different scenes.
      原文链接:
    • NSTL
    • Elsevier

    A two-step framework for dispatching shared agricultural machinery with time windows

    Wang Y.-J.Huang G.Q.
    13页
    查看更多>>摘要：? 2021 Elsevier B.V.Nowadays, farmers and even governments are faced with increasing yields with limited resources, and the extensive use of agricultural machinery is one of the most efficient methods for that. Agricultural machinery usually charges high prices, and it is economically impractical for small-scale farmers to afford them. The shared agricultural machinery is racing ahead full throttle in the market. Farmers can submit a usage request to a shared agricultural machinery company, and the company would then dispatch their machines to farmers to provide operational service. While this new business mode has shown promising benefits, there are new operational challenges. Agricultural production is a strictly seasonal process, and the yield would be affected by working time. Thus, operators would be assigned continuous tasks caused by the overlap of time windows. With a large number of demands, it is challenging for operators to dispatch shared agricultural machinery with time windows efficiently. This study develops a novel two-step dispatching framework for shared agricultural machinery with time windows. In the first step, a model-based spatiotemporal clustering approach is developed to cluster farmlands according to their location, time windows, and crop strain. The shortest route within each cluster of farmlands is also determined. In the second step, shared agricultural machines are routed across the clusters to minimize the dispatching costs. These two steps are formulated as Mixed Integer Linear Programming models, and a two-step heuristic based on CPLEX is proposed to solve these problems. Numerical experiments are conducted with large-scale data from a real-world shared agricultural machinery company. Our computational experiments demonstrate the efficiency, effectiveness, and practicality of the developed approach.
      原文链接:
    • NSTL
    • Elsevier

    An interpretable deep learning approach for calibration transfer among multiple near-infrared instruments

    Yang W.Yang J.Zhang X.Xu J....
    9页
    查看更多>>摘要：? 2021 Elsevier B.V.Spectroscopic techniques have been widely applied in agricultural applications. The development of calibration transfer is promising for the robust analysis of spectral data collected by varying instruments. The reliance on standard samples for standardization approaches remains a critical challenge for on-site applications. In this study, a deep learning approach, named DeepTranSpectra (DTS), is proposed to transfer convolutional neural network models among multiple near-infrared spectrometers with different types. The proposed DTS approach effectively avoids the requirements for standard samples by using labeled samples of slave instruments. The calibration transfer analysis is investigated on a soybean meal and one wheat dataset for predicting moisture and crude protein contents. The developed DTS approach demonstrates improved transfer performance compared with three popular standardization approaches, including piecewise direct standardization (PDS), canonical correlation analysis (CCA), and slope and bias correction (SBC). A feature visualization method is leveraged to interpret the transfer mechanism of the DTS approach. The interpretation results show that the DTS approach refines the model parameters to adapt to slave devices based on critical features of the master calibration. The DTS approach provides advanced reliability under different sample selections in Monte-Carlo cross-validation. The integration of deep learning approaches with calibration transfer analysis facilitates agricultural applications for emerging deep learning-based chemometric analysis.
      原文链接:
    • NSTL
    • Elsevier

    Detection and classification of tea buds based on deep learning

    Xu W.Li J.Shang S.Ding X....
    11页
    查看更多>>摘要：? 2021The detection and classification of tea is the premise to realize the automation and intelligence of the famous and high-quality tea picking. The tea buds and tender leaves, as the raw materials of famous and high-quality tea, have similar colors to older leaves, so tea buds can only be picked manually at present. To solve the problem of detection and classification of different grades of tea in mechanical picking for famous and high-quality tea, this paper proposes a detection and classification approach of a two-level fusion network with a variable universe. This approach combines the rapid detection ability of YOLOv3 and the high-accuracy classification ability of DenseNet201 to realize the accurate detection of tea buds. Furthermore, the influence of the shooting angle of the camera on the detection result is compared under the two conventional shooting styles, and the corresponding dataset is established for famous and high-quality tea. The experimental results show that the detection accuracy of the proposed approach is 95.71% for the side-shot tea buds, which is 10.60% higher than the detection accuracy of the top-shot tea buds. This research has certain theoretical and practical significance for intelligent and accurate picking of famous and high-quality tea.
      原文链接:
    • NSTL
    • Elsevier

    Investigation of optimal vegetation indices for retrieval of leaf chlorophyll and leaf area index using enhanced learning algorithms

    Verma B.Prasad R.Yadav S.A.Srivastava P.K....
    19页
    查看更多>>摘要：? 2021With the availability of high-resolution data due to sensor technology advancement, it is now easier for researchers and scientists to detect or view the spectral variability of different crops. For this study, Leaf chlorophyll content (LCC) and Leaf area index (LAI) of the crops Maize (Zea mays), Mustard (Brassica), and pink Lentils (Lens esculenta) under different irrigation and fertilizer treatments have been analyzed. In total, rigorous assessment of 25-hyperspectral vegetation indices (VIs) at both leaf and canopy level for chlorophyll content, whereas 7- hyperspectral VIs for LAI at canopy level were computed to investigate the robustness of these VIs for LCC and LAI assessment. Variable importance in projection (VIP) using Partial Least Square regression (PLSR) and coefficient of determination (R2) were computed for all the VIs to extract the most sensitive information for the retrieval of LCC and LAI. As a result, the VIs using the red-edge reflectance bands at 705 and 750 nm were found highly responsive to LAI compared to other wavebands. In contrast, the VIs indices made of green (550 nm), red (670, 690, and 700 nm), and red-edge (705, 750 nm) bands were found highly sensitive to the temporal LCC values of lentils and maize crop beds. In addition, the temporal LCC values of Mustard crop beds’ were found sensitive to the VIs made of green (550 nm), red (670, 690, and 700 nm), and NIR (800 nm) wavebands. The three VIs having high VIP and R2 values were selected as optimum sets of input to build support vector regression models using radial (SVR-Rad), linear (SVR-Li), polynomial (SVR-Poly), Random Forrest Regression (RFR), Partial least square regression (PLSR), and Hybrid neural fuzzy inference system (HyFIS). The analysis showed that the SVR-Rad model outperformed the SVR-Li, SVR-Poly, RFR, PLSR, and HyFIS models in terms of robustness for biophysical and biochemical parameters retrieval using hyperspectral data.
      原文链接:
    • NSTL
    • Elsevier

    Temperature conditions during commercial transportation of cull sows to slaughter

    Thodberg K.Foldager L.Fogsgaard K.K.Gaillard C....
    8页
    查看更多>>摘要：? 2021Management of ambient temperature is central in sow production, but temperature in commercial trucks transporting sows has only received limited scientific focus. We aimed to describe temperature inside trucks depending on season and on shifts in state of the trucks (‘non-stationary’, ‘stopped’, ‘waiting’) transporting sows (up to 8 h) to slaughter. Temperature outside the trucks was described during the last part of the journeys, and compared with temperature inside the trucks. Data were collected during 12 months and included 39 commercial journeys. We measured temperature (°C) inside trucks every minute in one compartment holding sows at commercial density. Data for temperature outside the trucks were collected from an official weather station near the slaughterhouse. Mean journey duration was 233 ± 114 min (range 47–470 min). From GPS-data, 26 of the journeys involved at least one stop lasting 5 min or more. Stops had a mean duration of 14 ± 8 min (range 5–40 min). Mean temperature inside trucks during journeys was 13.8 ± 5.3 °C (3.3–26.0), and differed between seasons. During journeys, temperature inside trucks increased during stationary periods. When compared to the outside temperature during 10 min before arriving at the slaughterhouse, and in the first 10 min of waiting after arrival, the mean temperature difference between inside and outside the truck ranged from 3 to 7 °C. The results suggest that in a moderate climatic zone like Denmark, temperature in trucks transporting sows to slaughter was not maintained within the thermal comfort zone of sows, and increased during stationary periods. This may have adverse consequences in terms of animal welfare. Future studies of optimization of transport management, ventilation and logistics are needed to examine possibilities to maintain temperatures in the comfort zone.
      原文链接:
    • NSTL
    • Elsevier

    Intelligent IoT-multiagent precision irrigation approach for improving water use efficiency in irrigation systems at farm and district scales

    Jimenez A.-F.Cardenas P.-F.Jimenez F.
    19页
    查看更多>>摘要：? 2021 Elsevier B.V.The fourth industrial revolution in agriculture seeks the automation of traditional practices, using modern smart technologies. Advances in electronics, computation and the internet of things are integrated for improving field inputs management. The aim of this paper is to present the design and implementation of an intelligent IoT-multiagent precision irrigation approach for improving water use efficiency in irrigation systems. The study site was the large-scale irrigation and drainage district of Chicamocha and Firavitoba (Usochicamocha) located in Boyacá - Colombia, where water is distributed from the Chicamocha riverbed. In the proposed system, irrigation is supervised and controlled in each field by an intelligent irrigation agent that autonomously prescribes and applies water amounts with agronomical criteria. The methodology was applied with real (cyber-physical) and virtual (simulated) intelligent agents and was extended to eleven pump stations that supply water to 5911 fields. Using a MQTT protocol, hundreds of irrigation intelligent agents report water prescriptions and crop characteristics to a master agent in each pump station, who creates a regional irrigation map to manage georeferenced field information and performs negotiation of water resources between agents according to supply availability. Field maps and intelligent irrigation agents can be visualized using devices with internet access. Results demonstrated that irrigation amounts were correctly applied on the fields, thus improving the water use efficiency. This technology is a novel support to decision-making in water resources management applications at field and district scales.
      原文链接:
    • NSTL
    • Elsevier