查看更多>>摘要：? 2022 Elsevier B.V.The sounds emitted by various insect species are highly specific and, thus, can be used as a way to acoustically characterize them. Consequently, acoustic insect detection has been widely studied by the scientific community in the field of pattern recognition. In Brazil, the cicada species Quesada gigas is considered a pest in coffee plantations, because the insects feed on the sap of the plants and can cause losses to farmers in mass attacks. Based on the fact that the most striking feature of cicadas is the emission of sounds for breeding purposes, this paper presents an alternative algorithm for acoustic detection of cicadas. The algorithm combines sound feature extraction with feature analysis based on Empirical Mode Decomposition (EMD) and Paraconsistent Feature Engineering (PFE), respectively, followed by a classification step based on a Support Vector Machine (SVM). Specifically, a study on the influence of eight EMD stopping criteria on the classification of sounds is presented. The results show that the proposed methodology can obtain accuracy values above 98% considering the Energy Difference Tracking (EDT) stopping criterion, vectors with 18 features and at least 46% of the vectors for SVM training. In the computational cost aspect, the stopping criterion Standard Deviation (SD) stands out, providing accuracy values above 96.67% for vectors with only two features. These results show that this study is feasible for Internet of Things applications, favoring the development of detection devices for field use with long-lasting autonomy. Technologies like these can enable the implementation of more and more daring projects involving Smart Farms and e-waste, aiming to reduce impacts to the environment. Suggestions for future work based on the PFE are also presented.
查看更多>>摘要：? 2022Disease has a significant impact on strawberry quality and yield, and deep learning has become an important approach for the detection of crop disease. To address the problems of complex backgrounds and small disease spots in strawberry disease images from natural environments, we propose a new Faster R_CNN architecture. The multiscale feature fusion network is composed of ResNet, FPN, and CBAM blocks, and it can effectively extract rich strawberry disease features. We built a dataset for strawberry leaves, flowers and fruits, and the experimental results showed that the model was able to effectively detect healthy strawberries and seven strawberry diseases under natural conditions, with an mAP of 92.18% and an average detection time of only 229 ms. The model is compared with Mask R_CNN and YOLO-v3, and we find that our model can guarantee high accuracy and fast detection operational requirements. Our method provides an effective solution for crop disease detection and can improve farmers' management of the strawberry growing process.
查看更多>>摘要：? 2022The behavior of pigeons in the dovecote reflects their environmental comfort and health indicators. In order to solve the problems of time-consuming, labor-consuming, and subjectivity of traditional manual experience, an improved YOLO V4 light-weight target detection algorithm was proposed for row detection of breeding pigeons. Employ SPP, FPN, and PANet networks to strengthen the features retrieved from GhostNet as the backbone. To ensure accuracy, Ghostnet-yolo V4 reduced the model's number of parameters and raised its size to 43 MB. The light-weight feature extraction network GhostNet outperformed MobileNet V1~V3 under the modified model. Faster RCNN, SSD, YOLO V4 and YOLO V3 compression rates were increased by 43.4 percent, 35.8 percent, 70.1 percent, and 69.1 percent, respectively. The improved algorithm has an accuracy of 97.06 percent and a recognition speed of 0.028 s per frame. The improved model can provide a theoretical foundation and technological reference for detecting breeding pigeon behavior in real-time in a dovecote.
查看更多>>摘要：? 2022 Elsevier B.V.The rapid development of intelligent and unmanned technology has been applied in various fields including aquaculture. Intelligent and unmanned technologies create new opportunities and challenges for the production of intelligent fisheries. This paper focuses on the four major aspects of deep-sea aquaculture including intelligent feeding, water quality detection, biomass estimation, and underwater inspection, reviews their development process from the traditional manual way to mechanization, then to automation, and finally to unmanned, intelligent equipment. The convenience, efficiency, and precision of each intelligent device in practical application are also discussed. In addition, the main factors limiting the large-scale application of intelligent and unmanned equipment in the aquaculture industry, as well as the future development trend of aquaculture robots are highlighted.
查看更多>>摘要：? 2022Alleviating the heat stress suffered by dairy cows in order to increase their productivity depends on an effective ventilation system. For this reason, this study evaluated the heat mitigating performance of a perforated air ducting (PAD) system with special attention given to significant design parameters (e.g., the shape and size of the orifices and the speed and flow rate of the air jets) having to do with the jet flow pattern and its corresponding cooling efficiency. More specifically, this work aimed to evaluate, using numerical simulation, the effects produced by four different types of air-jet orifice (i.e., three-circular orifice, single-circular orifice, rectangular orifice, and slot orifice) and also the effects produced by different jet flow rates and by the bulk movement of indoor air with respect to the jet properties and their cooling performance in relation to cows reclining in free stalls. The outcomes show that the jet emitted from the slot orifice distributes air more effectively over the surface of a reclining cow and produces a more even range of skin temperatures. The jet cooling performance achieved with a jet flow rate of 470.4 m3 h?1 (50% of the recommended ventilation rate for 625 kg cow) and a jet temperature of 28 °C was equivalent to the cooling performance achieved by a 2 m s?1 wind stream of the same air temperature passing over the model cow's body. Additionally, the convective heat dissipation rate of the reclining cow increased from 63.47 to 91.27 W m?2 as the jet flow rate increased from 212.0 to 564.5 m3 h?1, indicating that increasing the flow rate of a jet emitted through the slot orifice could improve the convective heat dissipation rate, but the rate of the increase in the convective heat transfer decreased simultaneously. The bulk movement of the indoor air (>0.2 m s?1) negatively affects jet flow and the corresponding cooling performance. Therefore, a PAD system equipped with slot orifices will most likely be more effective when atmospheric conditions are less agitated (such as those in naturally ventilated barns with minimal cross wind speeds or inside enclosed barns). In short, the findings of this study should warrant using the PAD system to ventilate free-stall dairy barns to mitigate heat stress of dairy cows.
查看更多>>摘要：? 2022 Elsevier B.V.The drying performance of a heat pump drying room primarily depends on the uniformity of the air distribution. The air distribution is affected by parameters such as circulating fan and condenser resistance. This study aims to improve the uniformity of the air distribution in a heat pump drying room. In this study, a three-dimensional simulation of the air distribution in a drying room was carried out using computational fluid dynamics software. A porous medium model and multiple reference frame model were adopted in this study. The results obtained from the models were in agreement with the experimental results, with a discrepancy of 7%. The simulation results showed that the airflow uniformity was poor at the fan outlet and opening of the clapboard. Moreover, a large amount of air flowed through the uppermost zone of the drying room. Subsequently, a type of wind deflector structure was designed to improve the uniformity of the airflow at the fan outlet, and the optimal separation size of the wind board was determined using numerical calculations. The standard deviation of the outlet airflow rate of the final diversion cover was 0.001. The optimised uniformity index of the wind speed in the drying room was 0.81, which was 8% higher than that of the original structure.
查看更多>>摘要：? 2022 Elsevier B.V.There are many maize seed varieties circulating on the Chinese market, some varieties will be added and eliminated by the government every year, and fake varieties cannot be exhaustively listed. Furthermore, the updating of some varieties requires retraining the entire model, which is time-consuming and laborious. This poses a huge challenge for the authenticity identification of varieties and online model updating. Herein, hyperspectral imaging (HSI) combined with incremental learning (IL) was used to solve this problem. A novel radial basis function-biomimetic pattern recognition (RBF-BPR) model for IL was proposed and compared with one-class support vector machine (OCSVM) and BPR models under two test schemes. Hyperspectral images of five varieties of maize seeds were collected, and convolutional autoencoder (CAE) was used to extract features to remove redundant information and improve the generalization ability of models. A unique hybrid model was designed for each variety respectively, and the IL process was simulated. In two test schemes, the overall correct acceptance rate (CAR) for the known varieties and the overall correct rejection rate (CRR) for the unknown varieties of CAE-RBF-BPR model both reached 100%, which was superior to CAE-OCSVM and CAE-BPR models. Especially after RBF was used to map data, the performance of RBF-BPR model had a qualitative improvement compared with the original BPR model. In summary, the proposed method can realize IL without accessing the old classes data, while meeting the requirements of identifying known varieties and rejecting unknown varieties. In addition, if some varieties are eliminated by the government in the future, the corresponding models can also be removed form the whole system. The combination of such method and HSI has a broad application prospect in the identification of maize seed varieties, thus avoiding the trouble of retraining entire model when some maize seed varieties need to be updated.
查看更多>>摘要：? 2022 Elsevier B.V.Apple fire blight is a common disease that seriously damages the growth of apple trees. It is urgent to detect the severity difference and spatial distribution of its infection in time and accurately. In this study, we used the DJI Matrice 200 multi-rotor UAV equipped with the MicaSense RedEdge-M multispectral camera to obtain the multispectral images of apple tree canopies. Then, based on the Minimum Redundancy Maximum Relevance (mRMR) algorithm, we selected the Ratio Vegetation Index (RVI), Anthocyanin Reflectance Index (ARI) and Triangular Vegetation Index (TVI) from 20 candidate vegetation indices as the optimal feature combinations. And then we use the isolation forest (iForest) algorithm to detect the abnormal values. The samples with the abnormal values removed were used as input to construct apple fire blight detection models using decision trees, Random Forest (RF) and Support Vector Machine (SVM) classification algorithms, respectively. The results show that the overall accuracy of the RF model reaches 94.0%, which is 6.0% and 10.0% higher than the SVM model and the decision tree model, respectively. The Kappa coefficient of the RF model is 0.904, which is the highest among all models, and the omission error and commission error are also the smallest. For healthy samples, slightly infected samples and seriously infected samples, the omission error and commission error were 0, 7.69%, 13.34%, 7.14% and 9.09%, 0, respectively. The research results prove the feasibility of UAV multi-spectral remote sensing images to detect apple fire blight.
查看更多>>摘要：? 2022 Elsevier B.V.Automatic body size measurement of livestock using 3D vision technology is one of the current research focuses. Point clouds obtained from freely walking livestock involve a variety of postures. However, measuring livestock body sizes requires their upright and straight standard postures, and non-standard measurement postures such as bending their body or lowering their head lead to inaccuracy in body size estimation. In this paper, we analyze 207 groups of point cloud data collected from 25 landrace pigs, and propose a standard posture classification algorithm. By using the algorithm based on the median skeleton extraction, 22 key points of the pig's skeleton were obtained and all joint point vector eigenvalue sets were calculated. Then Bagging-SVM Posture Classifier was constructed, and by three experts’ votes, comparisons were made between the automatic classification results and the manual classification results based on standard and non-standard postures of pigs. According to the automatic calculation of four parameters including body length, body height, body width and abdominal girth in standard and non-standard postures, the experimental data showed that the body size measurement results in standard postures presented high stability and consistency, while the results in non-standard postures fluctuated considerably, which significantly affected the accuracy and stability. The experiment showed that the ratio of standard postures to non-standard postures was 1:3. Finally, based on joint point vectors eigenvalues, the regression models were applied to adjust body length, body height, body width and abdominal girth in non-standard postures. The linear regression and nonlinear regression methods such as BP regression and SVR support vector regression were used for comparison. The experimental results indicated that various regression methods can significantly improve the accuracy of automatic measurement by correcting the pig's body size measurement results in non-standard postures. Therefore, it is essential to classify and adjust the livestock point cloud postures to improve the accuracy of three-dimensional measurement.
查看更多>>摘要：? 2022 Elsevier B.V.Accurate estimates of the N concentration and biomass (W) in plant organs provide information regarding the mechanisms of N distribution, which is critical for improving N use efficiency (NUE) and optimizing N management. Dimensionless passive remotely sensed data may lead to the asymptotic saturation problem when extracting W, whereas commercial lidar systems employing only one wavelength band have limited capacity in N retrieval. Combining the advantages of passive remote sensing and traditional lidar, hyperspectral lidar (HSL) has the ability to simultaneously extract structural and spectral information for plants. The objectives of this research were to evaluate the ability of HSL to estimate maize N concentration and W at the organ level, and to test whether HSL can characterize maize N distribution at different growth stages and under different N fertilizer conditions. A wide range of HSL performance for leaf and stem N extraction (R2 = 0.71 – 0.91) was observed based on the partial least squares regression (PLSR) method with spectral indices as inputs. Close relationships (R2 ≥ 0.75) were established between extracted height metrics (stem height and plant height) and organ-level W. The W portioning, dynamics of N concentration, and the variations in N with W accumulation were successfully monitored based on the estimated N concentration and W, thus demonstrating the capacity of HSL to characterize the N distribution pattern within maize plants. Our results show that the novel HSL system holds much potential for monitoring plant N distribution and serving precision agriculture.
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