Computers and Electronics in Agriculture最新文献

{"title":"Predicting the 3D bone structure of pork shoulders using X-ray imaging and statistical shape modeling","authors":"Michiel Pieters ,&nbsp;Pieter Verboven ,&nbsp;Bart M. Nicolaï","doi":"10.1016/j.compag.2025.110666","DOIUrl":"10.1016/j.compag.2025.110666","url":null,"abstract":"<div><div>In the meat industry, deboning and cutting are essential processing steps often performed by humans. To automate these processes it is essential to understand the complexity and variability of their three-dimensional (3D) shape as well as the relationship between their outer shape and inside bone structure. In this paper, we introduce a 3D statistical shape model (SSM) that describes the outer surface of a pork shoulder and its corresponding inner bone structure. X-ray computed tomography (CT) scans were acquired from 45 right-hand side and 45 left-hand side pork shoulders. The CT scans were segmented to obtain 3D models of the external shape and internal bone structure. Surface meshes were then created and used for establishing SSMs of the outer surface, the bone structure and the combined surfaces of the left and right pork shoulders based on principal component analysis. The first five of a total of 40 principal components were able to describe 63.6 % of the variability in the entire dataset. The mean absolute error (MAE) of the proposed fitting method in this paper was 5.15 mm for the test set. Besides being compact, the models could also generate realistic 3D shapes of pork shoulders that were not present in the dataset. These shapes can be used in the development of automated cutting and deboning procedures, and, thus, lead to improved precision in cutting, reduced waste, and further enhancements of automation within the meat industry.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110666"},"PeriodicalIF":7.7,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570757","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":"Advancing animal farming with deep learning: A systematic review","authors":"Zahid Ur Rahman,&nbsp;Mohd Shahrimie Mohd Asaari,&nbsp;Haidi Ibrahim","doi":"10.1016/j.compag.2025.110674","DOIUrl":"10.1016/j.compag.2025.110674","url":null,"abstract":"<div><div>Deep learning has revolutionized animal farming by enabling automated health monitoring, behavior analysis, and livestock management. This review examines the application of key deep learning architectures, including convolutional neural networks (CNNs), You Only Look Once (YOLO), memory-based neural networks (MBNNs), and generative adversarial networks (GANs), in various aspects of animal farming. These models have demonstrated success in tasks such as real-time livestock detection, disease prediction, activity monitoring, and animal identification. However, challenges such as occlusion, data scarcity, small training datasets, environmental variability, and imbalanced data remain significant barriers to model reliability and scalability. By analyzing one hundred seventeen articles following PRISMA guidelines, this review highlights recent advancements, identifies research gaps, and discusses solutions such as image augmentation, synthetic data generation, and domain adaptation. The findings emphasize the potential of deep learning to enhance precision farming while addressing critical challenges. Finally, future research directions are proposed to improve model generalization, integration with IoT-based monitoring systems, and real-time decision-making for sustainable and intelligent livestock management.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110674"},"PeriodicalIF":7.7,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570758","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":"Improving picking efficiency under occlusion: Design, development, and field evaluation of an innovative robotic strawberry harvester","authors":"Zixuan He ,&nbsp;Zibo Liu ,&nbsp;Zhiyan Zhou ,&nbsp;Manoj Karkee ,&nbsp;Qin Zhang","doi":"10.1016/j.compag.2025.110684","DOIUrl":"10.1016/j.compag.2025.110684","url":null,"abstract":"<div><div>Robotic harvesting has long been seen as the potential alternative to manual harvesting in the strawberry industry. However, despite much progress made in the harvesting process from detection to picking, these technologies are not yet commercially viable. One of the limiting factors for increased performance is fruit occlusion in canopies, particularly in the open-field environments. There has been only limited studies on active occlusion handling/removal techniques during robotic picking. This paper presents the development and evaluation of a strawberry harvesting robot focusing on occlusion handling in open-field environments using vision-based occlusion information and novel end-effector design. The robot was composed of an integrated machine vision system based on deep learning techniques, a 6 DOF manipulator, and an innovative end-effector equipped with a fan system, and a mobile platform. Based on the classification of detected strawberries (‘not occluded’ or ‘occluded’), the robotic platform followed specific steps for directly picking the strawberries (if not occluded) or removing/dispersing the occlusion over the strawberries (if occluded) and subsequently picking them. The effectiveness of this harvesting robot including fruit recognition &amp; localization, and picking method was evaluated using multiple experiments in both the simulation field and the real field. The results showed that the mean average precision in strawberry detection was 80.5% and classification accuracy was 93.2%. Picking efficiency of the robot was enhanced substantially by the use of fan system. In an outdoor strawberry field, the robot achieved a picking rate of 58.1% without fan system, which increased to 73.9% with the fan system (a 15.8% increase in fruit picking rate). It was found that the average processing time of machine vison system was 6.26 s and the overall average time to pick single strawberry with the fan system for removing occlusion was 20.1s.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110684"},"PeriodicalIF":7.7,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563269","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":"Triboelectric force feedback-based fully actuated adaptive apple-picking gripper for optimized stability and non-destructive harvesting","authors":"Jingwei Sun ,&nbsp;Linlin Sun ,&nbsp;Guangze Zhao ,&nbsp;Junsheng Liu ,&nbsp;Zixu Chen ,&nbsp;Linlong Jing ,&nbsp;Xinpeng Cao ,&nbsp;Hongjian Zhang ,&nbsp;Wei Tang ,&nbsp;Jinxing Wang","doi":"10.1016/j.compag.2025.110725","DOIUrl":"10.1016/j.compag.2025.110725","url":null,"abstract":"<div><div>Picking robots play a crucial role in promoting the efficient development of the apple industry. However, mechanized picking faces the dual challenge of ensuring picking effectiveness while minimizing fruit damage. This paper proposes a fully actuated adaptive apple-picking gripper based on triboelectric force feedback, achieving an optimized balance between grasping stability and low damage. The gripper employs biomimetic conical fingers inspired by the fin-ray effect, which were structurally reconstructed and optimized based on fish-fin architecture, mechanical modeling, and simulation experiments. A flexible force-sensing sensor based on a triboelectric nanogenerator (TENG) was developed and integrated into the fingers to enable precise force feedback perception and enhance the detachment capability of the gripper. The gripper adopts a four-finger fully actuated scheme that more closely mimics human grasping actions. The grasping space is precisely designed. Based on this threshold, the Claw-net neural network force feedback control system is developed. As a result, the hand achieved adaptive, damage-free grasping and real-time grasp state perception. Furthermore, this study proposed an innovative performance evaluation framework for clamping-type fruit-picking grippers, focusing on both grasping and detachment performance. Experimental results demonstrated that the integrated solution eliminated grasping-induced damage while achieving a picking success rate of over 98%, providing valuable insights for the development and application of low-damage mechanized apple picking technology.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110725"},"PeriodicalIF":7.7,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563709","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":"Optimizing citrus production accounting for decisions’ timeframes and stochastics","authors":"Christian Müller,&nbsp;Jochen Gönsch,&nbsp;Louisa Albrecht,&nbsp;Max Staskiewicz","doi":"10.1016/j.compag.2025.110440","DOIUrl":"10.1016/j.compag.2025.110440","url":null,"abstract":"<div><div>In many countries the citrus industry is an important economic factor. Unlike many supply chains, which are “demand-driven” systems, the orange industry’s supply chain is typically a “production-driven” system. Oranges are either sold directly to the market or to juice producers, which process oranges into juice. The resulting residue is either disposed of as waste or used to produce by-products that reduce waste and contribute to overall contribution margins.</div><div>In view of the changing climate and the resulting weather conditions a planting plan considering different types of orange trees is useful. For each orange variety, the planning process also includes decisions on the number of oranges that will be sold directly to final consumers, transported to juice producers, or stored and the by-products produced. The paper shows the potential of a stochastic, integrated strategic planning model that considers the temporal scope of different decisions. The proposed scenario-based linear programming model was developed to determine the amount of planted tress for each tree type and the production quantities of the different end products of the system (oranges, juice, and by-products) given the resources (fresh oranges, storage capacity) required to maximize the contribution margin of the company/system. The results of the scenario-based model are compared to the results of a worst-case approach, an on-average-approach, and an intuitive approach. In comparison, the scenario-based model always delivers the best results.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110440"},"PeriodicalIF":7.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548650","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":"MDS-PointPillars: A lightweight obstacle identification method in farmland based on three-dimensional LiDAR for autonomous navigation","authors":"Xianlu Guan ,&nbsp;Huan Wan ,&nbsp;Weikang Han ,&nbsp;Rui Jiang ,&nbsp;Yuanzhen Ou ,&nbsp;Yuli Chen ,&nbsp;Zhiyan Zhou","doi":"10.1016/j.compag.2025.110688","DOIUrl":"10.1016/j.compag.2025.110688","url":null,"abstract":"<div><div>To improve the autonomous navigation and operation of smart agricultural machinery in complex farmland, this study proposes a lightweight obstacle identification method, MDS-PointPillars, based on three-dimensional LiDAR to enhance perception capabilities. The MDS-PointPillars model primarily consisted of two components: the pillar feature net (PFN) and the backbone. In the PFN component, a multi-pooling encoding module (MPE) was designed, which integrated max-pooling, average-pooling, and attention mechanisms to improve the extraction of multi-scale point cloud features. In the backbone component, a depthwise separable convolution block (DSB) was designed to reduce computational complexity while enhancing the perception of both local and global features. Additionally, the model incorporated a parameters-free simple attention module (SimAM), which adaptively strengthened the focus on key point cloud features, further improving the identification accuracy of rare and hard-to-classify obstacles. Experimental results showed that MDS-PointPillars achieved a mean average precision (mAP) of 90.8 % on the test set of person, agricultural machinery, and utility poles in farmland, with an inference speed of 20.1 FPS and a model size of only 13.1 MB, significantly reducing computational burden. Robustness test revealed that the MDS-PointPillars maintained precision (P), recall (R), and mAP above 88.4 %, 87.2 %, and 88.6 %, respectively, across different scenarios, demonstrating its excellent adaptability and robustness in complex agricultural environments. Compared with mainstream models, MDS-PointPillars reduces parameters by 91.1 %, 76.1 %, and 73.2 %, and improves speed by 645.5 %, 70.8 %, and 368.6 % compared to Pv-RCNN, SECOND, and PointRCNN, respectively. This highlights its greater application potential in resource-limited farmland.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110688"},"PeriodicalIF":7.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556688","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 framework for estimating agricultural water requirements: Accounting for anthropogenic activities and climate change","authors":"Morteza Rahimpour ,&nbsp;Majid Rahimzadegan ,&nbsp;Amir AghaKouchak ,&nbsp;Taha B.M.J. Ouarda","doi":"10.1016/j.compag.2025.110729","DOIUrl":"10.1016/j.compag.2025.110729","url":null,"abstract":"<div><div>The present research introduces a framework titled the <em>Decision Support System for Water Resources and Agriculture under Anthropogenic Activities and Climate Change Impacts</em> (hereafter, AnthroClimDSS). This framework incorporates several methods: I) a two-step calibration of the Soil and Water Assessment Tool (SWAT) using streamflow data and Actual Evapotranspiration (ETa) estimation from the Surface Energy Balance Algorithm for Land (SEBAL); II) evaluation of fifty high-resolution daily downscaled CMIP6 global climate models (GCMs); and III) introduction of indicators to assess water supply risk, including the Agricultural Water Requirement Supply Index (AWRSI). A historical analysis of AWRSI in a sub-basin of Urmia Lake, Iran, reveals that, although agricultural water demand was fully supplied, significant pressure was placed on water resources, particularly groundwater, due to anthropogenic activities. Projections using GCMs within the AnthroClimDSS framework show that future climate change impacts are likely to be substantial. Continuing current practices could lead to severe water scarcity (possibly bankruptcy) and environmental degradation, as water demands increasingly outpace sustainable supply. However, scenario analysis suggests that the adverse impacts of both climate change and human activities can be mitigated through targeted agricultural water management at the farm scale. The proposed AnthroClimDSS framework offers a valuable tool for watershed planners to develop and implement more sustainable water resources management strategies.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110729"},"PeriodicalIF":7.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563704","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":"Estimating water use efficiency in maize: a UAV-based approach integrating multisensory data with SEBAL evapotranspiration modeling","authors":"Wushuai Chang ,&nbsp;Shenghao Gu ,&nbsp;Baiyan Wang ,&nbsp;Shuping Hu ,&nbsp;Ruiqi Li ,&nbsp;Xinyu Guo","doi":"10.1016/j.compag.2025.110695","DOIUrl":"10.1016/j.compag.2025.110695","url":null,"abstract":"<div><div>Rapid, accurate, and non-destructive estimation of crop water use efficiency (WUE) at the field scale is crucial not only for evaluating water efficient cultivars and practices in scientific research but also for optimizing irrigation schedule in agricultural production. The current lack of efficient methods for high-throughput phenotyping WUE hinders development of sustainable agriculture under globally intensified water scarcity. This study aimed to utilize unmanned aerial vehicle (UAV) multisensory remote sensing data combined with a process model to achieve rapid WUE determination via accurate daily-scale evapotranspiration and aboveground biomass (AGB) estimates. First, vegetation indices, canopy temperature, and canopy structural parameters were extracted from multispectral (MS), thermal imaging (TIR), and radar data and combined with an automated machine learning (AutoML) for AGB estimation. The beta function was then employed to accurately estimate AGB accumulation at a daily step (<em>AGB</em>_daily) over the entire growth period. The daily evapotranspiration (<em>ET</em>_daily) was calculated by the surface energy balance algorithm for land (SEBAL) model driven by MS, TIR, and meteorological data. Finally, the WUE was determined by the ratio of <em>AGB</em>_daily to <em>ET</em>_daily. Multisensory data fusion and further integration with process-based model proved effective for simultaneously estimating <em>AGB</em>_daily, <em>ET</em>_daily, and WUE with R² values of 0.71, 0.93, and 0.79, respectively. Notably, the proposed WUE estimation method can capture different temporal pattern between cultivars with different levels of tolerance to drought. We applied this approach to screen water efficient cultivars and found that appropriate reduction of irrigation can improve WUE. In conclusion, this study shows promising perspective in the use of a UAV-based approach integrating multisensory data with SEBAL evapotranspiration modeling for monitoring and evaluating water consumption and utilization in maize.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110695"},"PeriodicalIF":7.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548649","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 high-efficiency regulation method for optimal root zone temperature under different nitrogen fertilizer using discrete curvature","authors":"Huimin Li ,&nbsp;Ziyuan Wei ,&nbsp;Miao Lu ,&nbsp;Pan Gao ,&nbsp;Zhangtong Sun ,&nbsp;Huarui Wu ,&nbsp;Jin Hu","doi":"10.1016/j.compag.2025.110714","DOIUrl":"10.1016/j.compag.2025.110714","url":null,"abstract":"<div><div>Nitrogen (N) fertilization plays an important role in plant life activities and growth. To optimize N uptake and improve dry matter accumulation in plants, this study proposed a method to obtain optimal root zone temperatures (RZT) under different N levels. The experiment was designed with five N levels and five RZTs for tomato seedlings, and the chlorophyll fluorescence data of tomato leaves were measured at each treatment. Polynomial regression and elastic net regularization were used to fitting the response curve of Fv/Fo, which is indicated to the potential activity of photosystem Ⅱ (PSⅡ). The feature parameters of curvature on the response curve were considered as the targets for regulation and were calculated by the U-chord curvature method. The dynamic regulation model of suitable RZT of tomato was obtained by fitting the curvature feature parameters. The results of the verification experiment showed that the experimental group increased root dry weight by 20.60%-92.30%, stem dry weight by 9.09%-39.90%, leaf N content by 2.70%-6.15%, and leaf phosphorus content by 6.08%-8.59%. Therefore, RZT optimization has a key role in improving plant photosynthesis, plant N uptake efficiency and dry matter accumulation under different N fertilization conditions. The research is significant for RZT optimization of hydroponic crops with different nutrient supply in protected agriculture.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110714"},"PeriodicalIF":7.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518867","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":"Optimizing matrix barcode ground control points for automated location detection in UAS-based remote sensing","authors":"Karla S. Ladino,&nbsp;Michael P. Sama","doi":"10.1016/j.compag.2025.110717","DOIUrl":"10.1016/j.compag.2025.110717","url":null,"abstract":"<div><div>High spatial accuracy is essential in unmanned aircraft system (UAS)-based remote sensing for applications in precision agriculture. Traditional ground control points (GCPs) improve the spatial accuracy of measurements derived from imagery but typically require manual tagging, a process that is both time-intensive and susceptible to errors. Matrix barcodes offer an opportunity to automate georeferencing in UAS-based remote sensing by addressing the bottlenecks associated with traditional GCPs in photogrammetry. This study aimed to refine the encoding process of matrix barcode GCPs, with objectives that included (1) evaluating the performance of several barcode types for use in GCP applications; and (2) assessing different compression methods for encoding geographic coordinates within the barcodes. Field tests showed that Aztec and Micro QR codes provided more reliable recovery at higher altitudes—up to 45 m above ground level compared to standard QR codes—attributed to their larger module sizes. Statistical analysis identified altitude and barcode type as significant factors impacting barcode recovery, with Aztec and Micro QR codes showing a 55–57 % higher recovery rate compared to standard QR codes for 1.07 x 1.07 m GCPs. While statistical analysis found no significant differences among encoding methods, field tests indicated that binary encoding had relatively improved performance, particularly when paired with Aztec codes. However, environmental data collection did not fully support one-to-one comparisons across different days; as such, the results should be interpreted within the specific conditions of this study rather than as definitive conclusions. Overall, these findings offer insights into the feasibility of using matrix barcodes as an automated alternative to traditional GCPs, potentially improving the efficiency of UAS-based remote sensing workflows by reducing the labor, time, and risk of user error associated with manual GCP processing.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110717"},"PeriodicalIF":7.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523441","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}