Biosystems Engineering最新文献

{"title":"Semantics-aware next-best-view planning for efficient search and detection of task-relevant plant parts","authors":"Akshay K. Burusa,&nbsp;Joost Scholten,&nbsp;Xin Wang,&nbsp;David Rapado-Rincón,&nbsp;Eldert J. van Henten,&nbsp;Gert Kootstra","doi":"10.1016/j.biosystemseng.2024.09.018","DOIUrl":"10.1016/j.biosystemseng.2024.09.018","url":null,"abstract":"<div><div>Searching and detecting the task-relevant parts of plants is important to automate harvesting and de-leafing of tomato plants using robots. This is challenging due to high levels of occlusion in tomato plants. Active vision is a promising approach in which the robot strategically plans its camera viewpoints to overcome occlusion and improve perception accuracy. However, current active-vision algorithms cannot differentiate between relevant and irrelevant plant parts and spend time on perceiving irrelevant plant parts. This work proposed a semantics-aware active-vision strategy that uses semantic information to identify the relevant plant parts and prioritise them during view planning. The proposed strategy was evaluated on the task of searching and detecting the relevant plant parts using simulation and real-world experiments. In simulation experiments, the semantics-aware strategy proposed could search and detect 81.8% of the relevant plant parts using nine viewpoints. It was significantly faster and detected more plant parts than predefined, random, and volumetric active-vision strategies that do not use semantic information. The strategy proposed was also robust to uncertainty in plant and plant-part positions, plant complexity, and different viewpoint-sampling strategies. In real-world experiments, the semantics-aware strategy could search and detect 82.7% of the relevant plant parts using seven viewpoints, under complex greenhouse conditions with natural variation and occlusion, natural illumination, sensor noise, and uncertainty in camera poses. The results of this work clearly indicate the advantage of using semantics-aware active vision for targeted perception of plant parts and its applicability in the real world. It can significantly improve the efficiency of automated harvesting and de-leafing in tomato crop production.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"248 ","pages":"Pages 1-14"},"PeriodicalIF":4.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting wheat scab levels based on rotation detector and Swin classifier","authors":"Dongyan Zhang ,&nbsp;Zhipeng Chen ,&nbsp;Hansen Luo ,&nbsp;Gensheng Hu ,&nbsp;Xin-Gen Zhou ,&nbsp;Chunyan Gu ,&nbsp;Liping Li ,&nbsp;Wei Guo","doi":"10.1016/j.biosystemseng.2024.09.016","DOIUrl":"10.1016/j.biosystemseng.2024.09.016","url":null,"abstract":"<div><div>Wheat scab is a highly destructive disease that adversely impact wheat crops throughout their growth cycle. It is crucial to promptly evaluate the levels of wheat scab in the field to prevent its spread. Manual observation, however, is inefficient and time-consuming. Recent research has indicated that computer vision-based methods can enhance efficiency in this regard. This study proposed a method for predicting wheat scab levels using a rotation detector and Swin classifier.</div><div>To minimise background interference, the study incorporated the rotation wheat detector (RWD) network for detecting wheat heads. The RWD network employed the Kalman filter Intersection over Union (KFIoU) to predict the angle, thereby improving accuracy. The Swin wheat classifier (SWC) network was employed to classify healthy and diseased wheat heads. The SWC network benefited from the shifted window self-attention module (SW-MSA), which enhanced feature extraction by establishing connections with other windows. The proposed method was evaluated using wheat field images collected over 3 years. The results demonstrate promising performance, achieving a 96% accuracy in predicting wheat scab levels. Furthermore, the <em>R</em>^<em>2</em> and RMSE values for diseased wheat count were 97.62% and 3.61, respectively. This method offers an accurate means of predicting wheat scab levels through the analysis of wheat field images. Additionally, the introduction of the rotation detector presents a novel contribution to research on wheat scab detection.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"248 ","pages":"Pages 15-31"},"PeriodicalIF":4.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts of crate design, number of heat lamps and lying posture on the occurrence of shoulder lesions in sows","authors":"Shubham Bery ,&nbsp;Tami M. Brown-Brandl ,&nbsp;Gary A. Rohrer ,&nbsp;Sudhendu Raj Sharma ,&nbsp;Suzanne M. Leonard","doi":"10.1016/j.biosystemseng.2024.09.017","DOIUrl":"10.1016/j.biosystemseng.2024.09.017","url":null,"abstract":"<div><div>This study investigated the interaction of sow and engineering factors on shoulder lesion formation. Sows were randomly assigned to three farrowing crate designs: Traditional Stall Layout, Expanded Creep Stall Layout, and Expanded Sow &amp; Creep Stall Layout. Each crate configuration was further differentiated by the inclusion of either one (1HL) or two (2HL) heat lamps. Digital and depth images were collected from an overhead time of flight depth camera (Kinect V2) every 5 s. Computer vision techniques were employed to analyze top-down digital images from the 21st to the 24th day of farrowing to detect and estimate lesion size. Additionally, the study incorporated an analysis of sow lying behaviors on the occurrence and size of lesions using depth images. Sow's environmental and phenotypic data - weight, parity, body condition score, total lying time and number of lying transitions in a day were investigated for impact on shoulder lesion. The results indicated that the interaction of smaller crate sizes and increased heat lamp usage significantly impacted lesion occurrence (p &lt; 0.05). Also, higher parity and lighter weight sows showed higher lesion occurrence (p &lt; 0.05). However, other factors, such as the number of heat lamps alone and detailed metrics of lying postures, did not show a significant impact on lesion occurrence. In contrast, none of the studied factors showed a significant impact on the size of shoulder lesions. This highlights the importance of allocating crate space with respect to heat lamp placement to the sows.</div><div>Science4Impact Statement (S4IS): This manuscript evaluates shoulder lesions' presence and size in lactating sows housed within farrowing stalls. Shoulder lesions are one of the main causes of premature culling in sows and are a major concern for animal well-being. Understanding the impact of crate design and the number of heat lamps is important for the engineering design of the farrowing environment.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"247 ","pages":"Pages 249-256"},"PeriodicalIF":4.4,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterising equivalent droplet indicators of sprinkler irrigation from a kinetic energy perspective","authors":"Rui Zhang ,&nbsp;Yichuan Liu ,&nbsp;Delan Zhu ,&nbsp;Pute Wu ,&nbsp;Changjuan Zheng ,&nbsp;Xiaomin Zhang ,&nbsp;Nazarov Khudayberdi ,&nbsp;Changxin Liu","doi":"10.1016/j.biosystemseng.2024.09.019","DOIUrl":"10.1016/j.biosystemseng.2024.09.019","url":null,"abstract":"<div><div>Equivalent droplet velocity and diameter are important parameters for measuring the effectiveness of sprinkler spraying; however, non-optical test methods (paper stain, flour pellet, and oil immersion methods) can only obtain the droplet number and diameter. With the widespread use of optical instruments in sprinkler testing, droplet velocity can also be measured, therefore, it has become possible to calculate the average droplet characteristics from an energy perspective. This paper proposes an energy-weighted method for calculating droplet equivalence indicators. Statistical analyses were performed based on five types of sprinkler irrigation droplet distribution data to compare the characteristics and differences between the energy-weighted method and the calculation results of the other methods. The results showed that 1) the velocity outcomes of the energy-weighted droplet equivalent method, empirical formula I, and empirical formula II consistently increase and decrease; 2) the equivalent droplet diameter based on the energy-weighted method is the largest, followed by the equivalent method related to droplet volume, and the smallest is the equivalent method related to droplet quantity; and 3) the equivalent droplet velocity and diameter calculated by the energy-weighted equivalent method can characterise droplets with a high energy contribution. The energy-weighted equivalent droplet velocity and diameter indicators derived in this study provide new ideas for characterising droplet averaging.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"247 ","pages":"Pages 241-248"},"PeriodicalIF":4.4,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disturbance analysis and seeding performance evaluation of a pneumatic-seed spoon interactive precision maize seed-metering device for plot planting","authors":"Shidong Deng,&nbsp;Yamei Feng,&nbsp;Xiupei Cheng,&nbsp;Xianliang Wang,&nbsp;Xiangcai Zhang,&nbsp;Zhongcai Wei","doi":"10.1016/j.biosystemseng.2024.09.007","DOIUrl":"10.1016/j.biosystemseng.2024.09.007","url":null,"abstract":"<div><div>In response to the serious issue of missed seeding of the seed-metering device caused by the small and gradually decreasing number of maize seeds in plot planting conditions, a precision seed-metering device for maize plots with pneumatic-seed spoon interactive was designed. The seed-metering device utilises the coupling interaction between the seed spoons and airflow to adjust the maize posture in the filling zone, achieving stable filling of the seed-metering device with a low population of seeds in the seed chamber. EDEM software is used to simulate and analyse the disturbance caused by three types of seed-metering discs and the average kinetic energy in the filling zone as the evaluation criterion. A test platform for seed-metering device of maize plot was constructed, with the qualified index, multiple index, and missing index as evaluation criteria. A full-factorial experiment was conducted with rotation speed of the seed-metering disc, air pressure, and types of seed-metering discs as factors, determining the optimal seed-metering disc for seeding performance. The results indicated that under conditions of low seed population in the seeding chamber, with air pressures ranging from −1.5 to −2.5 kPa and seed-metering disc speeds between 1.16 and 3.49 rad s⁻¹, the seed-metering device with linear disturbance exhibited a multiple index of &lt;6.25% and a missing index of &lt;3.46%. Additionally, the qualified index consistently reached 90.29%. These evaluation criteria meet the standards, demonstrating effective seeding capabilities.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"247 ","pages":"Pages 221-240"},"PeriodicalIF":4.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Positioning of mango picking point using an improved YOLOv8 architecture with object detection and instance segmentation","authors":"Hongwei Li ,&nbsp;Jianzhi Huang ,&nbsp;Zenan Gu ,&nbsp;Deqiang He ,&nbsp;Junduan Huang ,&nbsp;Chenglin Wang","doi":"10.1016/j.biosystemseng.2024.09.015","DOIUrl":"10.1016/j.biosystemseng.2024.09.015","url":null,"abstract":"<div><div>Positioning of mango picking points is a crucial technology for the realisation of automated robotic mango harvesting. Herein, this study reported a visualised end-to-end system for mango picking point positioning using improved YOLOv8 architecture with object detection and instance segmentation, as well as an algorithm of picking point positioning. At first, the improved YOLOv8n model, incorporating the BiFPN structure and the SPD-Conv module, was utilised to enhance the detection performance of mango fruits and stems. This model achieved a detection precision of 98.9% in fruits and 97.1% in stems, with recall of 99.5% and 94.6% respectively. Then, the YOLOv8n-seg model was used for segment the stem ROI (Region of interest), leading to 81.85% in MIoU and 88.69% in mPA. Finally, a skeleton line of the stem region was obtained on the basis of the segmentation image, and a picking point positioning algorithm was developed to determine the coordinates of the optimal picking point. Subsequently, the positioning success rate of coordinates, absolute errors, and relative errors were calculated by comparing the automatic positioned coordinates with the manually positioned stem region. Experimental results indicated that this study achieved an average positioning success rate of 92.01%, with an average absolute error of 4.93 pixels and an average relative error of 13.11%. Additionally, the average processing time for processing 640 images using the picking point positioning system is 72.75 ms. This study demonstrates the reliability and effectiveness of positioning mango picking points, laying the technological basis for the automated harvesting of mango fruits.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"247 ","pages":"Pages 202-220"},"PeriodicalIF":4.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Laser Doppler vibrometer enables in-situ monitoring of peach firmness","authors":"Dachen Wang ,&nbsp;Yilei Hu ,&nbsp;Jiaqi Xiong ,&nbsp;Yibin Ying ,&nbsp;Ce Yang ,&nbsp;Di Cui","doi":"10.1016/j.biosystemseng.2024.09.013","DOIUrl":"10.1016/j.biosystemseng.2024.09.013","url":null,"abstract":"<div><div>Fruit firmness is a measure of the edible quality and maturity of peaches. In-situ monitoring of peach firmness can aid in fruit quality control and determining the optimal harvest time according to market demand. In this study, a non-contact acoustic vibration-based method was proposed for in-situ monitoring of fruit firmness of on-tree peaches. A new design of a compressed air excitation unit was constructed to impact the peach on the tree and a laser Doppler vibrometer was adopted to measure the acoustic vibration response (AVR) of the peach. To isolate the vibration information characterising fruit firmness, the AVR was firstly pre-processed by the wavelet threshold denoising method and then analysed by the autoregressive method to acquire the power spectral density (PSD) of the peach. For effectively extracting vibration features from the PSD to predict peach firmness, a novel one-dimensional convolutional neural network (CNN_m) with multiscale perceptual fields was constructed. The performance of CNN_m was compared with those of partial least squares regression, support vector regression models, and a single-branch 1D-CNN model with the mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (<em>R</em>^<em>2</em>), and residual prediction deviation (<em>RPD</em>). The results indicated that the proposed method enabled in-situ monitoring of peach firmness and the established CNN_m model performed better than other models in predicting peach firmness (<span><math><mrow><msubsup><mi>R</mi><mi>P</mi><mn>2</mn></msubsup></mrow></math></span> = 0.813, MAEP = 1.636 N mm⁻¹, RMSEP = 2.501 N mm⁻¹, and <span><math><mrow><msub><mrow><mi>R</mi><mi>P</mi><mi>D</mi></mrow><mi>P</mi></msub></mrow></math></span> = 2.334).</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"247 ","pages":"Pages 191-201"},"PeriodicalIF":4.4,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new removal method of yellow-rotten leaf for hydroponic lettuce with the flipping-tearing-twisting trajectory and its parameters optimisation","authors":"Yidong Ma ,&nbsp;Chong Qi ,&nbsp;Liming Zhou ,&nbsp;Xin Jin ,&nbsp;Bo Zhao ,&nbsp;Xinping Li","doi":"10.1016/j.biosystemseng.2024.09.010","DOIUrl":"10.1016/j.biosystemseng.2024.09.010","url":null,"abstract":"<div><p>To intelligently remove the yellow-rotten leaf of hydroponic lettuce, a new leaf removal method was proposed. The lettuce position was adjusted for yellow-rotten leaf removal according to the visual recognition and localisation, and then the adsorbed yellow-rotten leaf was lifted by air pipes. Finally, the leaf was clamped and removed along the flipping-tearing-twisting trajectory. The adsorbing pressure, adsorbing position, and clamping position for yellow-rotten leaf removal were confirmed by the adsorbing and stretching tests. To improve the leaf removal success rate, the tearing angle, flipping angle, and torsional time radio were optimised by Box-Behnken tests. A quadratic model for the three factors and leaf removal success rate was established to analyse the orders of significance, and the order of significance for single factor was (i) the tearing angle, (ii) the flipping angle, and (iii) the torsional time radio. The order of significance for interaction terms was (i) the flipping angle and tearing angle, and (ii) the flipping angle and torsional time ratio. The solved optimal combination of factors was a flipping angle of 100.5°, a tearing angle of 131.0°, and a torsional time ratio of 0.68, which gave the maximum leaf removal success rate. The optimal combination of factors was verified, and the leaf removal process was shot by high speed camera. The verification tests showed that the maximum leaf removal success rate was 82.8%, and the leaf removal process took 6.58 s, meeting the requirements of yellow-rotten leaf removal for hydroponic lettuce.</p></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"247 ","pages":"Pages 175-190"},"PeriodicalIF":4.4,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact damage evolution rules of maize kernel based on FEM","authors":"Han Tang ,&nbsp;Guixuan Zhu ,&nbsp;Zhiyuan Sun ,&nbsp;Changsu Xu ,&nbsp;Jinwu Wang","doi":"10.1016/j.biosystemseng.2024.09.012","DOIUrl":"10.1016/j.biosystemseng.2024.09.012","url":null,"abstract":"<div><p>The main cause of damage to maize during harvesting and processing is impact damage. This study aimed to investigate the evolution of impact damage to maize kernels under different impact velocities and orientations. Based on the damage characteristics observed in impact tests, an elastoplastic model has been established to accurately simulate the damage behaviour of maize kernels. The microscopic impact behaviour of maize kernels was presented by the finite element method. The results indicated that there were differences in the evolution of damage for different damage morphology in maize kernels. The nature of surface damage was the diffusion and reflection of stress waves, while the nature of local breakage was the concentration of tiny cracks and the release of elastic potential energy. The nature of fracture was the combined effect of compressive and tensile stresses. Meanwhile, under the surface damage, the maximum stresses in the contact area of maize kernels subjected to front orientation were 20.08 MPa, 10.71 MPa for side orientation, and 13.56 MPa for bottom orientation. Under the local breakage, the front orientation with the highest number of cracks occurred at a velocity of 27.3 m s⁻¹, while for the side orientation, it occurred at 24.6 m s⁻¹, and for the bottom orientation, it occurred at 26.2 m s⁻¹. The results can be extended to the study of impact damage in irregularly shaped grains, which was beneficial for controlling product quality and optimising the design of relevant mechanical parameters in agricultural engineering and food engineering fields.</p></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"247 ","pages":"Pages 162-174"},"PeriodicalIF":4.4,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of a hyperspectral image pipeline toward building a generalisation capable crop dry matter content prediction model","authors":"Ioannis Malounas,&nbsp;Borja Espejo-Garcia,&nbsp;Konstantinos Arvanitis,&nbsp;Spyros Fountas","doi":"10.1016/j.biosystemseng.2024.09.009","DOIUrl":"10.1016/j.biosystemseng.2024.09.009","url":null,"abstract":"<div><p>Hyperspectral imaging has proven to be a reliable technique for estimating dry matter, a common variable when considering the quality of the fresh produce. However, developing models capable of generalising across different crops is challenging. In this study, several pipelines were explored towards achieving a robust and accurate generic regression model were evaluated and the development of Automatic Relevance Determination (ARD) and Partial Least Squares (PLS) algorithms for fruit and vegetable dry matter estimation. The models were built using a VIS-NIR dataset that includes both fruit and vegetables, namely, apples, broccoli and leek (n = 779). The PLS regression model obtained Root Mean Square on Prediction (RMSEP) = 0.0137, outperforming ARD regression (RMSEP = 0.0140) on a 10x5-fold cross-validation protocol. The evaluated preprocessing techniques affect the two regression algorithms differently, with the best results achieved when the pipeline was used without feature extraction. Overall, the pipeline using either ARD or PLS regression shows strong performance and generalisation for Visible-Near Infrared (VIS-NIR)-based dry matter estimation across diverse fruits and vegetables.</p></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"247 ","pages":"Pages 153-161"},"PeriodicalIF":4.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}