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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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    Rice row tracking control of crawler tractor based on the satellite and visual integrated navigation

    Ma Z.Yin C.Du X.Zhang G....
    14页
    查看更多>>摘要：? 2022 Elsevier B.V.To achieve autonomous navigation of the crawler tractor in the paddy field, a set of control system was developed by integrating the satellite and visual navigation. The system is mainly composed of a satellite module, a visual module and a controller. The satellite module detects the deviation information between the tractor's position and the pre-set line AB using GPS Real–time Kinematic (RTK) technology and the navigation is achieved according to the preview point tracking algorithm. The visual module obtains the rice crop images in real time by camera, and the binary image segmentation is completed by ExG(2G-R-B), Otsu and mask method. Then the region search algorithm is used to classify the region of each rice row, and the visual navigation path is fitted by the weighted least square method, and the weight value is based on the lookahead distance. Finally, the preview point tracking algorithm is used to realize the visual navigation. Furthermore, the control division line between the satellite navigation and visual navigation is established, the threshold value of which is determined based on the crop row deviation due to the satellite navigation when rice crops were planted. The crawler tractor navigation tests were conducted to evaluate the performance of the integrated navigation system. When the speed of the tractor was 0.4～0.6 m/s, 0.7～0.9 m/s and 1.0～1.2 m/s, the maximum lateral deviation of the tracking by visual navigation was 0.18 m, 0.15 m and 0.14 m, respectively, and the RMSE was 55 mm, 64 mm and 60 mm, respectively. Under the same speed, when the threshold value was set to 0.15 m, the maximum lateral offset of the tracking by integrated navigation was 0.14 m, 0.13 m and 0.12 m, respectively, and the RMSE was 41 mm, 50 mm and 55 mm, respectively. The experimental results indicate that the satellite navigation module in the integrated navigation system functions well in rice row tracking when some seedlings are missing, and significantly reduces the navigation deviation and the degree of deviation fluctuation.
      原文链接:
    • NSTL
    • Elsevier

    Fusion of acoustic and deep features for pig cough sound recognition

    Shen W.Ji N.Yin Y.Dai B....
    7页
    查看更多>>摘要：? 2022 Elsevier B.V.The recognition of pig cough sound is a prerequisite for early warning of respiratory diseases in pig houses, which is essential for detecting animal welfare and predicting productivity. With respect to pig cough recognition, it is a highly crucial step to create representative pig sound characteristics. To this end, this paper proposed a feature fusion method by combining acoustic and deep features from audio segments. First, a set of acoustic features from different domains were extracted from sound signals, and recursive feature elimination based on random forest (RF-RFE) was adopted to conduct feature selection. Second, time-frequency representations (TFRs) involving constant-Q transform (CQT) and short-time Fourier transform (STFT) were employed to extract visual features from a fine-tuned convolutional neural network (CNN) model. Finally, the ensemble of the two kinds of features was fed into support vector machine (SVM) by early fusion to identify pig cough sounds. This work investigated the performance of the proposed acoustic and deep features fusion, which achieved 97.35% accuracy for pig cough recognition. The results provide further evidence for the effectiveness of combining acoustic and deep spectrum features as a robust feature representation for pig cough recognition.
      原文链接:
    • NSTL
    • Elsevier

    A novel apple fruit detection and counting methodology based on deep learning and trunk tracking in modern orchard

    Fang W.Sun X.Gao F.Wu Z....
    11页
    查看更多>>摘要：? 2022 Elsevier B.V.Accurate count of fruits is important for producers to make adequate decisions in production management. Although some algorithms based on machine vision have been developed to count fruits which were all implemented by tracking fruits themselves, those algorithms often make mismatches or even lose targets during the tracking process due to the large number of highly similar fruits in appearance. This study aims to develop an automated video processing method for improving the counting accuracy of apple fruits in orchard environment with modern vertical fruiting-wall architecture. As the trunk is normally larger than fruits and appears clearly in the video, the trunk is thus selected as a single-object tracking target to reach a higher accuracy and higher speed tracking than the commonly used method of fruit-based multi-object tracking. This method was trained using a YOLOv4-tiny network integrated with a CSR-DCF (channel spatial reliability-discriminative correlation filter) algorithm. Reference displacement between consecutive frames was calculated according to the frame motion trajectory for predicting possible fruit locations in terms of previously detected positions. The minimum Euclidean distance of detected fruit position and the predicted fruit position was calculated to match the same fruits between consecutive video frames. Finally, a unique ID was assigned to each fruit for counting. Results showed that mean average precision of 99.35% for fruit and trunk detection was achieved in this study, which could provide a good basis for fruit accurate counting. A counting accuracy of 91.49% and a correlation coefficient R2 of 0.9875 with counting performed by manual counting were reached in orchard videos. Besides, proposed counting method can be implemented on CPU at 2 ～ 5 frames per second (fps). These promising results demonstrate the potential of this method to provide yield data for apple fruits or even other types of fruits.
      原文链接:
    • NSTL
    • Elsevier

    Visual navigation path extraction of orchard hard pavement based on scanning method and neural network

    Duan J.Yu J.Wang H.Yang Z....
    11页
    查看更多>>摘要：? 2022 Elsevier B.V.The accurate extraction of navigation path is very important for the automatic navigation of agricultural robots. Aiming at the complex orchard environment and the problem that the existing navigation path extraction algorithms are too complex and narrow application range, a visual navigation path extraction method based on neural network and pixel scanning was proposed in this paper. This method trained the semantic segmentation network based on Segnet and Unet on the basis of orchard road condition data sets. According to the edge of the orchard road condition mask area, the navigation path was fitted by the designed scanning method, filtering algorithm and weighted average method. The experimental results showed that the segmentation accuracy of neural network under low light, ordinary light and strong light was 96.00%, 92.00% and 92.00% respectively. The average pixel error was 9.5 pixel and the average distance error was 5.03 cm. In the actual orchard environment, the orchard road was generally 3.0 m, the average distance error accounted for 1.67%. Therefore, this method improves the accuracy of orchard visual navigation path extraction, meeting the operation requirements of tracked robots in orchard, and provides an effective reference for visual navigation task.
      原文链接:
    • NSTL
    • Elsevier

    Digital mapping of soil biological properties and wheat yield using remotely sensed, soil chemical data and machine learning approaches

    Jahandideh Mahjenabadi V.A.Asadi Rahmani H.Khavazi K.Rezaei M....
    15页
    查看更多>>摘要：? 2022 Elsevier B.V.Mapping of soil properties by using novel machine learning (ML) algorithms and optimized environmental covariates is of great importance for agricultural management to enhance crop production. This research aimed at evaluating ML algorithms to predict spatial distribution of soil biological properties and wheat yield in the Southwest of Iran. Topsoil samples (0–30 cm) were collected from a total of 60 sampling locations and wheat grain yield (plot 1 × 1 m) was recorded at each location. Soil properties including urease (Ur), alkaline phosphatase (AP), basal respiration (BR), microbial biomass carbon (MBC), soil organic carbon (SOC), MBC:SOC ratio, and metabolic quotient (qCO2) were measured. At the first step, Random Forest (RF) model was employed to predict soil biological properties by using terrain attributes, remote sensing indices and soil properties as covariates. In this step, both Variance Inflation Factor (VIF) and Pearson regression were applied to select the most important covariates in predicting soil biological properties and to decrease the dimension of the input space with considering no reduction in prediction accuracy. Secondly, wheat grain yield was modeled using six ML algorithms; they were optimized and evaluated in Caret package with 10-fold cross validation. Results showed the highest prediction accuracy for qCO2 (R2adj = 0.80) and the lowest for BR (R2adj = 0.23). Compared to environmental predictors, soil covariates had a greater effect in modeling Ur, qCO2, MBC and MBC:SOC ratio, while, for AP and BR, bands 6 and Chanel Network Base Level were the most important factors, respectively. In prediction of wheat grain yield, both Stochastic Gradient Boosting (SGB) and RF models outperformed with R2adj of 0.89 and 0.88, respectively. Results indicated that the Ur and AP played the major roles in predicting wheat grain yield and explaining its spatial variability. Our modeling results suggested that soil biological properties and yield can be estimated easily with reasonable accuracy. Overall, their high resolution maps may be useful for decision makers, stakeholders and applicants in agricultural management practices towards precision agriculture.
      原文链接:
    • NSTL
    • Elsevier

    LIBS in agriculture: A review focusing on revealing nutritional and toxic elements in soil, water, and crops

    Ren J.Zhao Y.Yu K.
    16页
    查看更多>>摘要：? 2022 Elsevier B.V.In recent years, rapid urbanization and industrialization have caused severe damage to the agro-ecological environment, such as heavy metal pollution of soil, eutrophication, and pollution of water, and excessive harmful substances in crops. Traditional atomic spectroscopy detection techniques, like atomic absorption spectroscopy (AAS), atomic fluorescence spectroscopy (AFS), atomic mass spectrometry (AMS), and X-ray fluorescence spectroscopy (XRFS), enable high-quality detection of agricultural pollution, but they are time-consuming and labor-intensive. Laser Induced Breakdown Spectroscopy (LIBS) technology is widely applied in agro-ecological monitoring because of its strengths in situ and non-destructive, no (little) sample pre-processing, fast and accurate. This review summarizes the applications of LIBS in agriculture in the last five years from 2017 to 2021. The detailed contents of the review are as follow: (a) analysis of soil and fertilizers elemental composition, (b) water quality detection, and (c) pests, diseases, and elements in crops. Lastly, the challenges and developing prospects of LIBS in agriculture are presented.
      原文链接:
    • NSTL
    • Elsevier

    Tomato disease and pest diagnosis method based on the Stacking of prescription data

    Xu C.Ding J.Zhang L.Qiao Y....
    13页
    查看更多>>摘要：? 2022Crop prescription data contains an extensive amount of information on crops, environment and pests, and has notable diagnostic capabilities. At present, there is lack of feasible methods for efficiently mining crop prescription data to perform accurate diagnoses. In view of the above problems, the purpose of our study is to mine prescription data information and assist the accurate diagnosis of crop diseases. In this paper, six tomato diseases and pests, namely, the tomato virus disease, tomato late blight, tomato gray mold, aphid, thrips and whiteflies, were explored to construct a diagnosis model based on prescription data mining. Original prescription data was subjected to pre-processing, text labeling and one-hot coding. The recursive feature elimination (RFE) method was then employed to extract 37 key features relating to crop diseases and pests from original 50 features. We constructed a tomato disease and pest diagnosis model based on two-stage Stacking ensemble learning to improve the diagnosis accuracy. The experimental results demonstrated the proposed diagnosis model in this paper exhibits a slightly superior performance compared to the best model (LGBM) among ten diagnosis models. The optimal Stacking model is composed of two layers: base-classifiers including GDBT, XGBoost and LGBM, and meta-classifier RF. The diagnosis accuracy of the proposed model for the tomato virus disease reached 94.84%, with an F1-score of 95.98% and overall accuracy of 80.36%. It also performed well on the multi-classification metrics: Macro avg (Precision: 76.55%, Recall: 78.17%, F1-score: 77.05%) and Weighted avg (Precision: 80.96%, Recall: 80.36%, F1-score: 80.50%). Moreover, following feature selection, the Stacking-based diagnosis model can reduce the running time by 12.08% with unchanged diagnosis accuracy. The proposed diagnosis model meets the real-world diagnosis requirements. This work provides new research concepts and a methodological foundation for future crop disease and pest diagnosis.
      原文链接:
    • NSTL
    • Elsevier

    Modeling of Sitophilus oryzae (L.) (Coleoptera: Curculionidae) based on historical weather data indicates aeration is effective for management of wheat stored in Greece

    Morrison W.R.Wilson L.T.Arthur F.H.Athanassiou C.G....
    11页
    查看更多>>摘要：? 2022An in-silico study was conducted to determine the feasibility of using aeration to manage Sitophilus oryzae (L.) in stored wheat throughout the country of Greece. Daily high and low temperature data were obtained for sixteen representative sites from 2010 to 2019 and averaged to predict hours below aeration temperature thresholds of 15, 18, and 21 °C from August–November. The sixteen sites were classified into distinct aeration zones based on temperature, which was associated with differences in latitude and elevation. In-silico predictions were developed for S. oryzae population growth in unaerated wheat, wheat aerated starting 15 July, and wheat aerated based on aeration triggering temperatures of 15, 18, and 21 °C. The optimal scenarios for inhibition of S. oryzae growth in wheat stored in the warmest Zone 1 is aeration triggered at 21 °C, but fumigations may be required in this zone. Aeration starting 15 July at 15 °C or aeration at 18 and 21 °C provides the best suppression of population growth for wheat stored in Zone 2, an intermediate climatic zone. For the coolest Zone 3, any aeration scenario is equally effective in suppressing S. oryzae. For all zones, predicted S. oryzae populations in unaerated wheat increase exponentially in late autumn to catastrophic levels, far exceeding the generally accepted level of 2 live adults per kg of wheat for export. The results presented herein can be used to develop aeration-based wheat management in Greece and may possibly lead to reduced reliance on fumigants for control of S. oryzae.
      原文链接:
    • NSTL
    • Elsevier

    Real-time defect inspection of green coffee beans using NIR snapshot hyperspectral imaging

    Chen S.-Y.Chiu M.-F.Zou X.-W.
    14页
    查看更多>>摘要：? 2022 Elsevier B.V.Coffee beans are important agricultural commodities traded in the international market. Screening for defective beans is an important step before roasting. The main types of defective beans include black, fermented, moldy, insect damaged, shell, and broken. Insect-damaged beans are the most common type of defective beans. Previously, coffee beans were sorted manually, which was extremely labor intensive and prone to fatigue-induced errors, resulting in inconsistent quality. This study combines a near-infrared snapshot hyperspectral sensor and deep learning to create a multimodal real-time coffee-bean defect inspection algorithm (RT-CBDIA) for sorting defective green coffee beans. Furthermore, three convolutional neural networks (CNN) were designed to achieve real-time inspection, i.e., lean 2D-CNN, 3D-CNN, and 2D–3D-merged CNN. Subsequently, principal component analysis was used to select important bands. Our experimental results achieved an overall accuracy of 98.6% using 1026 green coffee-bean samples. Furthermore, the RT-CBDIA achieved a Kappa value of 97.2% and real-time sorting speeds. These achievements are considerably beneficial for subsequent applications and the commercialization of smart agriculture. Our main objective is to commercialize the proposed RT-CBDIA algorithm by combining it with a robot to create a comprehensive yet affordable coffee-bean real-time inspection system. It can be used to achieve real-time and noninvasive inspections while reducing labor costs. In the future, our real-time inspection system can also be applied to other crops to ultimately advance smart agriculture.
      原文链接:
    • NSTL
    • Elsevier

    A convolutional operation-based online computation offloading approach in wireless powered multi-access edge computing networks

    Wang Y.Li M.Ji R.Zhang Y....
    11页
    查看更多>>摘要：? 2022Prompt, uninterrupted, accurate and reliable agricultural information plays an important role for agricultural decision making, which requires stable and efficient data transmission frame. Wireless powered multi-access edge computing (MEC) has recently emerged as a promising paradigm to improve the capability of data transmission with low-power network. Applying wireless powered MEC to agricultural information monitoring will benefit the development of smart agriculture. Management and scheduling of different applications (i.e., computing offloading) is one of the most important influencing factors for the performance of wireless powered MEC network. Of these, the mutual interferences of different WDs are an important factor which should be considered. To address this issue, an online computation offloading method based on convolutional operation is presented in this paper. And a fundamental wireless powered MEC network including one access point (AP) and multiple WDs is constructed to validate the efficacy of this approach. Impacts of three factors (including network size (i.e., the number of WDs), training intervals and memory size) and different application scenarios are studied and their influences on the performance of convolutional operation-based approach are analyzed. Additionally, convolutional operation-based method is compared with the other two offloading approaches (including DROO (a Deep Reinforcement learning-based Online Offloading) and CD (Coordinate Descent) algorithm). The results indicate that the convolutional operation-based approach is more suitable for large-scale wireless powered MEC networks (e.g., the number of WDs is more than 30) with moderate memory size (=512) and training interval (=50).
      原文链接:
    • NSTL
    • Elsevier