A novel vision-based weakly supervised framework for autonomous yield estimation in agricultural applications

Bellocchio, Enrico; Crocetti, Francesco; Fravolini, Mario Luca; Valigi, Paolo

doi:10.1016/j.engappai.2021.104615

Cited by 16 publications

(12 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Those that are available are mainly focused on monitoring and PA tasks (Y. Lu & Young, 2020), such as crop/weed discrimination, plant phenotyping, leaf detection, canopy volume calculation (Potena et al, 2020), fruit counting (Bellocchio et al, 2020), and yield estimation (Bellocchio et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

“…The majority of indoor data sets are collected in industrial buildings (Burri et al, 2016) or offices (Klenk et al, 2021) using handheld sensor kits, sensors mounted on a backpack (Wen et al, 2019) or unmanned robotic platforms (Shi et al, 2020). Outdoor urban data sets are usually collected using a sensorized car (Blanco-Claraco et al, 2014;Choi et al, 2018;Geiger et al, 2013;Kim et al, 2020) or a wheeled robot (Smith et al, 2009). In the last few years, there is also a growing interest in data sets built using virtual urban environments (Cabon et al, 2020;Gaidon et al, 2016).…”

Section: Urban Scenarios Data Setsmentioning

confidence: 99%

“…Although different publicly available localization and mapping data sets are available for indoor or urban outdoor scenarios, data sets for autonomous robotic tasks in agricultural environments are less common. Those that are available are mainly focused on monitoring and PA tasks (Y. Lu & Young, 2020), such as crop/weed discrimination, plant phenotyping, leaf detection, canopy volume calculation (Potena et al, 2020), fruit counting (Bellocchio et al, 2020), and yield estimation (Bellocchio et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

ARD‐VO: Agricultural robot data set of vineyards and olive groves

Crocetti

Bellocchio

Dionigi

et al. 2023

Journal of Field Robotics

Self Cite

View full text Add to dashboard Cite

The availability of real-world data in agricultural applications is of paramount importance to develop robust and effective robotic-based solutions for farming operations. In this application context, however, very few data sets are available to the community and for some important crops, such as grapes and olives, they are almost absent. Therefore, the aim of this paper is to introduce and release ARD-VO, a data set for agricultural robotics applications focused on vineyards and olive cultivations. Its main purpose is to provide the researchers with a real-world extensive set of data to support the development of solutions and algorithms for precision farming technologies in the aforementioned crops.ARD-VO has been collected with an unmanned ground vehicle (UGV) equipped with different heterogeneous sensors that capture information essential for robot localization and plant monitoring tasks. It is composed of sequences gathered in 11 experimental sessions between August and October 2021, navigating the UGV for several kilometers in four cultivation fields in Umbria, a central region of Italy. In addition, to highlight the utility of ARD-VO, two application case studies are presented. In the first one, the data set is used to compare the performance of simultaneous localization and mapping and odometry estimation methods using vision systems, light detection and ranging, and inertial measurement unit sensors. The second one shows how the multispectral images included in ARD-VO can be used to compute Normalized Difference Vegetation Index maps, which are crucial to monitor the crops and build prescription maps.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Urban Scenarios Data Setsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

ARD‐VO: Agricultural robot data set of vineyards and olive groves

Crocetti

Bellocchio

Dionigi

et al. 2023

Journal of Field Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

“…They focus more on the supervised learning, where the local data is usually well and correctly labeled. Nonetheless, such an assumption is rather unrealistic in numerous applications and services since labeling is considered time-consuming and costly [15]- [18]. Practically, users are more likely to have a certain amount of labeled data that they can easily obtain and another amount of partially-labeled data that they have difficulty in fully labeling [13], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Dual-Objective Personalized Federated Service System With Partially-Labeled Data Over Wireless Networks

Ching

Chang

Kuo

et al. 2023

IEEE Trans. Serv. Comput.

View full text Add to dashboard Cite

Federated learning (FL) emerges to mitigate the privacy concerns in machine learning-based services and applications, and personalized federated learning (PFL) evolves to alleviate the issue of data heterogeneity. However, FL and PFL usually rest on two assumptions: the users' data is well-labeled, or the personalized goals align with sufficient local data. Unfortunately, the two assumptions may not hold in most cases, where data labeling is costly, or most users have no sufficient local data to satisfy their personalized needs. To this end, we first formulate the problem, DoLP, that studies the issue of insufficient and partially-labeled data on FL-based services. DoLP aims to maximize two service objectives: 1) personalized classification objective and 2) the personalized labeling objective for each user within the constraint of training time over wireless networks. Then, we propose a PFL-based service system DoFed-SPP to solve DoLP. The DoFed-SPP's novelty is two-fold. First, we devise an inference-based first-order approximation metric, similarity ratio, to identify the similarity between users' local data. Second, we design an approximation algorithm to determine the appropriate size and set of users for uploading in each round. Extensive experiments show DoFed-SPP outperforms the state-of-the-art in final accuracy and time-to-accuracy performance on CIFAR10/100 and DBPedia.

show abstract

“…Anderson et al [26] compared several orchards in fruits counts and indicated that the DCNNs-based methods gave a better result than transitional methods. In addition, few other specific deep learning-based methods were designed to simplify the model building for better estimation performance [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Culling Double Counting in Sequence Images for Fruit Yield Estimation

et al. 2022

View full text Add to dashboard Cite

Exact yield estimation of fruits on plants guaranteed fine and timely decisions on harvesting and marketing practices. Automatic yield estimation based on unmanned agriculture offers a viable solution for large orchards. Recent years have witnessed notable progress in computer vision with deep learning for yield estimation. Yet, the current practice of vision-based yield estimation with successive frames may engender fairly great error because of the double counting of repeat fruits in different images. The goal of this study is to provide a wise framework for fruit yield estimation in sequence images. Specifically, the anchor-free detection architecture (CenterNet) is utilized to detect fruits in sequence images from videos collected in the apple orchard and orange orchard. In order to avoid double counts of a single fruit between different images in an image sequence, the patch matching model is designed with the Kuhn–Munkres algorithm to optimize the paring process of repeat fruits in a one-to-one assignment manner for the sound performance of fruit yield estimation. Experimental results show that the CenterNet model can successfully detect fruits, including apples and oranges, in sequence images and achieved a mean Average Precision (mAP) of 0.939 under an IoU of 0.5. The designed patch matching model obtained an F1-Score of 0.816 and 0.864 for both apples and oranges with good accuracy, precision, and recall, which outperforms the performance of the reference method. The proposed pipeline for the fruit yield estimation in the test image sequences agreed well with the ground truth, resulting in a squared correlation coefficient of R2apple = 0.9737 and R2orange = 0.9562, with a low Root Mean Square Error (RMSE) for these two varieties of fruit.

show abstract

A novel vision-based weakly supervised framework for autonomous yield estimation in agricultural applications

Cited by 16 publications

References 44 publications

ARD‐VO: Agricultural robot data set of vineyards and olive groves

ARD‐VO: Agricultural robot data set of vineyards and olive groves

Dual-Objective Personalized Federated Service System With Partially-Labeled Data Over Wireless Networks

Culling Double Counting in Sequence Images for Fruit Yield Estimation

Contact Info

Product

Resources

About