Smart agricultural technology最新文献

{"title":"YSD-BPTrack: A multi-object tracking framework for calves in occluded environments","authors":"Wangli Hao,&nbsp;Chao Ren,&nbsp;Yulong Fan,&nbsp;Meng Han,&nbsp;Fuzhong Li","doi":"10.1016/j.atech.2025.100876","DOIUrl":"10.1016/j.atech.2025.100876","url":null,"abstract":"<div><div>The accurate tracking of individual calves is essential for health monitoring. However, existing multi tracking frameworks often encounter frequent ID abnormal switching issues during occlusion. To address these challenges, we propose a novel multi-object tracking framework named YSD-BPTrack for calves in occluded environments on cattle farms in this paper. This framework mainly consists of two stages: detection and tracking. Concerning the detection phase, the DCNv4 is integrated into the YOLOv8s model to capture spatial deformation features caused by occlusion, thereby enhancing detection performance under occlusion. Additionally, the Star operation of StarNet is also applied to the model to achieve excellent detection performance with lower computational costs. Concerning the tracking stage, we first propose an innovative rematching algorithm (Rematching module) and a new trajectory removal strategy (Trajectory removal module). The Rematching module performs rematching with detection boxes utilizing extended trajectory prediction boxes in cases of occlusion, resulting in a reduced probability of ID switch errors. Moreover, the Trajectory Removal module dynamically adjusts the removal time for lost matching trajectories, which decreases the likelihood of trajectories being mistakenly removed. Specifically, our proposed novel framework achieves a HOTA (Higher Order Tracking Accuracy) of 91.6%, surpassing other frameworks in both track accuracy and efficiency. Experimental results also validate the superiority of the YSD-BPTrack, with HOTA increasing by 17.6%, MOTA (Multiple Object Tracking Accuracy) by 13.9%, MOTP (Multiple Object Tracking Precision) by 1.8%, <span><math><mi>ID</mi><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> (Identification <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> Score) by 15.4%, and reducing parameters by 49.1%, IDSw (Identification Switches) by 88.9%, and computational overhead by 39.2% compared to the other frameworks. Overall, the proposed multi-object tracking framework has great potential to revolutionize the tracking of calves.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100876"},"PeriodicalIF":6.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of the FERTI-drip model for the evaluation and simulation of fertigation events in drip irrigation","authors":"Manuel Morcillo,&nbsp;Miguel Ángel Moreno,&nbsp;Rocío Ballesteros,&nbsp;Rocío Arias,&nbsp;José Fernando Ortega","doi":"10.1016/j.atech.2025.100803","DOIUrl":"10.1016/j.atech.2025.100803","url":null,"abstract":"<div><div>FERTI-DRIP tool was developed to support decision-making in the fertigation process. The model determines the distribution of fertilizers at the subunit level, along with emitter discharge. FERTI-DRIP enables users to optimize the design of the irrigation subunit and the type and discharge of the installed emitter. Additionally, it helps determine the injection, advance, and cleaning times of fertilizer. Despite all these capacities, FERTI-DRIP has not been validated in a real irrigation system. Thus, the objective of this work has been to develop and implement a methodology to evaluate the actual distribution of fertilizers in a commercial productive environment and to calibrate FERTI-DRIP for this purpose.</div><div>This methodology has been applied in a commercial farm of 9 ha of almond trees (<em>Prunus dulcis</em> var. avijor), which is equipped with a drip irrigation system, intensively analyzing a fertigation event under real operating conditions.</div><div>This study has enabled the validation of the FERTI-DRIP tool, developing a methodology for evaluating fertigation systems that characterizes the distribution of fertilizers and their dynamics during an irrigation event. With the validation of FERTI-DRIP, a real fertigation process has been simulated, achieving a maximum time lag of three minutes and differences in the amount of fertilizer applied by each emitter of &lt;1 %. This enables decision-making regarding fertilizer application times. The model has accurately predicted the dose of fertilizer, the application and cleaning times at the dripper level, thereby improving application efficiencies and mitigating issues arising from salt accumulation in the subunit through proper time management.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100803"},"PeriodicalIF":6.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral bands vs. vegetation indices: An AutoML approach for processing tomato yield predictions based on Sentinel-2 imagery","authors":"Nicoleta Darra ,&nbsp;Borja Espejo-Garcia ,&nbsp;Vassilis Psiroukis ,&nbsp;Emmanouil Psomiadis ,&nbsp;Spyros Fountas","doi":"10.1016/j.atech.2025.100805","DOIUrl":"10.1016/j.atech.2025.100805","url":null,"abstract":"<div><div>Predicting tomato yield is crucial for agricultural production planning, management, market supply, and risk management. While yield prediction in precision agriculture presents a complex challenge, advancements in remote sensor technologies have greatly improved the accuracy and feasibility of these predictions. In this paper, three modeling approaches were implemented to estimate processing tomato yield based on satellite data, covering 152 fields across two growing periods. Sentinel-2 spectral bands and Vegetation Indices (VIs) captured at 5-day intervals during the growth periods, used as input parameters in an AutoML pipeline. The first approach aimed to identify the optimal timeframe and the most effective spectral bands and VIs, such as NDVI and RVI. Modelling results indicated that Red/Red Edge/NIR bands performed well in predicting yield, with the period between 75 to 90 days post-transplanting identified as the optimal timeframe for yieldpredictions (R² of 0.56). The second approach incorporated inter-date VIs, utilizing bands from different dates, leading to a significant improvement in performance with an R² of 0.61 and root mean square error (RMSE) of 12ton/ha. The third approach involved band combinations to enhance performance, where specific bands, including the Bands 4, 6, and 12, collectively achieved the highest R² of 0.65. Using feature extraction algorithms such as PCA, UMAP, and autoencoder partially contributed to improved performance while using the same VIs on consecutive different dates. By utilizing a higher number of bands and dates without the constraint of a VI formula demonstrated the potential for enhanced model accuracy.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100805"},"PeriodicalIF":6.3,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors influencing learning attitude of farmers regarding adoption of farming technologies in farms of Kentucky, USA","authors":"Dipesh Oli,&nbsp;Buddhi Gyawali,&nbsp;Shikha Acharya,&nbsp;Samuel Oshikoya","doi":"10.1016/j.atech.2025.100801","DOIUrl":"10.1016/j.atech.2025.100801","url":null,"abstract":"<div><div>Understanding farmers’ learning attitudes towards agricultural technologies and the factors affecting their adoption behavior is crucial in today's era of rapid technological advancement. This study focuses on assessing various socioeconomic factors influencing farmers’ learning behavior regarding technologies in farming operations and understanding farmers’ views on the future of precision agriculture in crop and livestock production. The study also examined the preferred methods farmers use for learning. Using R-studio, the binary logistic regression model was employed to analyze the survey data. The results suggest that the education level and social media use significantly affected farmers’ learning attitudes. In contrast factors such as gender, age, income level, related expertise, and farming experience had no significant impact. The study also concluded that seminars, workshops, and training are preferred learning methods. Thus, it is recommended that federal and state agencies and universities' extension systems should focus on combining these preferred learning methods with various social media platforms to disseminate the necessary information, knowledge, and skills to farmers, supporting better adoption of agricultural technologies.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100801"},"PeriodicalIF":6.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision agriculture for iceberg lettuce: From spatial sensing to per plant decision making and control","authors":"William Rohde,&nbsp;Fulvio Forni","doi":"10.1016/j.atech.2025.100797","DOIUrl":"10.1016/j.atech.2025.100797","url":null,"abstract":"<div><div>Precision agriculture enables growers to sense growth in the field and take actions at a high resolution. In this context, agriculture can be considered a feedback system, and feedback control algorithms are a promising approach for decision-making at a per-plant level. We present a control approach to assign per-plant nitrogen prescriptions for iceberg lettuce, with the objective of reducing variability at harvest and, as a result, increasing yield. The per-plant nitrogen inputs were generated using a proportional consensus-protocol style controller and non-destructive measurements of growth. This resulted in smaller lettuce plants receiving an increased dose of nitrogen and oversized plants receiving a decreased dose. In this paper, we present the results of three in-field trials applying the controller to real plants with manual applications. Our results demonstrated a reduction in the plant mass variance of outdoor crop in two of our three farm trials (32.6% and 19.7% lower variance in comparison to cohorts with a typical approach). This is a promising result, which could be improved in future work by developing a more accurate proxy measurement for crop growth and accounting for soil nitrogen.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100797"},"PeriodicalIF":6.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil and crop response to varying planter's downforce in corn and cotton fields","authors":"Luan Oliveira ,&nbsp;Brenda Ortiz ,&nbsp;Gregory Pate ,&nbsp;Thomas Way ,&nbsp;Rouverson Silva","doi":"10.1016/j.atech.2025.100798","DOIUrl":"10.1016/j.atech.2025.100798","url":null,"abstract":"<div><div>Effective planter downforce is crucial for optimizing seed placement and improving crop emergence and growth. This study explores the impact of different downforce settings on soil compaction, seeding depth, and crop performance for corn and cotton. Two field experiments were implemented using a John Deere 6145R tractor and a 6-row John Deere planter equipped with a DeltaForce® hydraulic downforce system in Shorter, Alabama, during the 2020 growing season. Multiple planter's downforce levels (0, 550, 1100, and 1800 N) were tested in both static and dynamic modes across two fields with distinct soil types (clay loam and sandy loam). Results indicated that the dynamic downforce mode provided more accurate and consistent load distribution compared to the static mode, which often exceeded target loads. Increased downforce led to deeper seeding depths, particularly with the dynamic mode, and higher loads in the static mode resulted in greater variability. For corn in clay loam soil, higher static downforce improved seed-to-soil contact, enhancing emergence and plant height. Conversely, cotton in sandy loam soil showed no significant differences in emergence or plant height, likely due to soil moisture conditions and target depth. The study concludes that dynamic downforce systems offer superior load control and uniformity, enhancing corn emergence and growth in heavier soils, while further research is recommended to optimize settings for various crops and soil conditions.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100798"},"PeriodicalIF":6.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PCCNet: A point supervised dense Chickens flock counting network","authors":"Yubin Guo ,&nbsp;Zhipeng Wu ,&nbsp;Zhiwei Su ,&nbsp;Jiangsan Zhao ,&nbsp;Ximing Li","doi":"10.1016/j.atech.2025.100795","DOIUrl":"10.1016/j.atech.2025.100795","url":null,"abstract":"<div><div>In broiler breeding, precise counting is crucial for improving production efficiency and ensuring animal welfare. Nevertheless, counting chickens precisely is a challenging task especially when young chicks always huddle for warmth. Although deep learning has been widely taken in different counting related tasks, more accurate localization and counting of chickens in high stocking density scenes still has not been well investigated. We propose a point supervised dense chickens flock counting network (PCCNet), which directly utilizes points as learning targets. The network adopts information feature fusion to assist the identification of broilers high stocking density scenes. In addition, considering the distance of neighboring points as matching cost in point matching algorithms is advantageous for generating more reasonable matching results, facilitating model convergence. To validate the effectiveness of the proposed network, a Chicken Counting Dataset (CCD) is built, consisting of two subsets separated by different ages: CCD_A and CCD_B. The accuracies of PCCNet on the two subsets of CCD are 97.85% and 97.06%, with corresponding Mean Absolute Errors (MAE) of 1.966 and 5.173, and Root Mean Square Errors (RMSE) values of 3.474 and 7.034, respectively. Our model achieves better broiler counting performance than other state-of-the-art (SOTA) methods.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100795"},"PeriodicalIF":6.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the Performance of YOLOv9t Through a Knowledge Distillation Approach for Real-Time Detection of Bloomed Damask Roses in the Field","authors":"Farhad Fatehi,&nbsp;Hossein Bagherpour,&nbsp;Jafar Amiri Parian","doi":"10.1016/j.atech.2025.100794","DOIUrl":"10.1016/j.atech.2025.100794","url":null,"abstract":"<div><div>Harvesting Damask roses by hand is especially challenging because of the thorns on their stems, which not only complicate the process but also pose a risk of injury to workers. This problem highlights the need for automation solutions to facilitate the harvesting process. To carry out agricultural operation, particularly for picking fully bloomed Damask roses, using harvesting robots offers significant potential to reduce labor costs while simultaneously improving crop quality. Recent developments in deep learning algorithms, especially in convolutional models, have shown significant promise for object detection, highlighting strong possibilities for improving the efficiency of this process. The substantial computational demands and processing times associated with many deep learning models present a significant obstacle to their implementation in real-time applications. To address this challenge, knowledge distillation (KD) has emerged as a valuable model compression technique. This approach enables complex \"teacher\" models to pass essential insights to more streamlined \"student\" models, making them more suitable for immediate, real-world applications. In this study, we trained YOLOv9t model as a student model and YOLOv9c model as a teacher model. To facilitate this learning, two different techniques including online distillation (OD) and offline distillation (OFD) were explored. The results demonstrated that applying both online and offline KD increased the mAP0.5 of YOLOv9t by 0.3% and 0.2%, respectively, and boosted the detection speed by 5.1 and 1.8 frames per second (FPS), respectively. The results showed that the YOLOv9t model, trained as a student with both OD and OFD methods, performed better than the YOLOv9t model. This distilled version of YOLOv9t shows strong potential as an effective model for real-time detection of fully bloomed Damask roses.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100794"},"PeriodicalIF":6.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated precision weighing: Leveraging 2D video feature analysis and machine learning for live body weight estimation of broiler chickens","authors":"Mairead Campbell ,&nbsp;Paul Miller ,&nbsp;Katerine Díaz-Chito ,&nbsp;Sean Irvine ,&nbsp;Mary Baxter ,&nbsp;Jesús Martínez Del Rincón ,&nbsp;Xin Hong ,&nbsp;Niall McLaughlin ,&nbsp;Thianantha Arumugam ,&nbsp;Niamh O'Connell","doi":"10.1016/j.atech.2025.100793","DOIUrl":"10.1016/j.atech.2025.100793","url":null,"abstract":"<div><div>The measurement of bird live weight during the production cycle is an important management practice in commercial broiler farming. However, the accuracy and practicalities of current weighing methods are limited. This paper proposes a non-invasive system that uses low-cost, overhead cameras combined with computer vision and AI techniques to automatically weigh broiler chickens. The main objectives were to: (i) evaluate 2D video feature descriptors, together with regression modelling, to predict the live weight of broilers; (ii) establish the impact of posture (i.e. sitting/standing) and bird age on the accuracy of weight estimation; (iii) assess the feasibility of the camera-weighing system to monitor weight at different bird ages. In the first experiment, a video feature analysis was performed to evaluate the accuracy of 2D feature descriptors (ellipse axes, ellipse area, bounding box width, bounding box height) to predict the weight of broilers. Individual birds were manually weighed to establish a reference weight. The relationship between the feature sets and the reference weight was evaluated using six multivariate regression models. The approach was tested on two groups of broilers aged 23 (<em>n</em> = 21 broilers) and 35 (<em>n</em> = 23 broilers) days old, weighing between 570 and 2980 g. In experiment 2, the best performing feature set and linear regression modelling from experiment 1 were applied to a larger number of birds across a greater age range (5 to 35 days old, <em>n</em> = 222 broilers). To be more representative of the intended application of this technology, footage was recorded from the feeding area of a commercial broiler house and an automated chicken detector and tracking method was applied. The model was retrained using reference weights from experiment 2 (ranging from 100 to 3085 g) to refine model performance. In experiment 1, the posture feature did not improve weight estimation whilst age improved the performance of all models. The accuracy of body weight estimation was greatest when bird age and the <em>minor ellipse axis</em> (x,y endpoints of the maximum points that are perpendicular to the longest line that can be drawn through an object) were used as model features. In experiment 2, the model showed the poorest performance in 5-day old birds with a mean relative error of 12.1 ± 7.9 %. Overall, the model could estimate the weight of a broiler chicken with a mean relative error of 7.0 ± 5.8 %. The results indicate that the analysis of 2D image features using video analytics and regression modelling is a promising method of obtaining rapid, cost-effective and accurate estimates of broiler live weight.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100793"},"PeriodicalIF":6.3,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on kiwifruit harvesting robot worldwide: A solution for sustainable development of kiwifruit industry","authors":"Zhiwei Tian ,&nbsp;Xiangyu Guo ,&nbsp;Wei Ma ,&nbsp;Xinyu Xue","doi":"10.1016/j.atech.2025.100792","DOIUrl":"10.1016/j.atech.2025.100792","url":null,"abstract":"<div><div>Kiwifruit harvesting is labor-intensive, and social issues like an aging population and a declining agricultural workforce have significantly increased costs, presenting unprecedented challenges to the industry. Automatic harvesting systems utilizing multi-sensor fusion, AI, and automation technologies show great potential for replacing manual labor in kiwi harvesting. This paper reviews over 140 research articles related to kiwi fruit harvesting robots, summarizing existing progress in two key areas: target fruit recognition and positioning systems, and fruit picking and collection systems. We compare the pros and cons of various methods, including traditional image recognition and deep learning, active and passive localization techniques, diverse end-effector design structure and driving mechanisms, robotic arm path planning, and harvesting systems. The results show that challenges remain in the commercialization of kiwi harvesting robots. The absence of a unified evaluation standard for robot performance makes the latest research achievements hard to be inherited, leading to slow advancements. Current algorithms are often not lightweight enough for low-cost embedded systems. Additionally, the reliance on manual labeling of dense targets and the accumulation of system error compromise the robustness of target recognition and spatial positioning in open environments. The existing studies tend to focus on local improvements rather than the entire harvesting system. So addressing these issues should be a priority for future research. This paper can provide a reference for researchers and assist industry professionals in understanding the trends in harvesting robot development.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100792"},"PeriodicalIF":6.3,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}