Automatic visual estimation of tomato cluster maturity in plant rows

Tenório, Gabriel Lins; Caarls, Wouter

doi:10.1007/s00138-021-01202-9

Cited by 9 publications

(3 citation statements)

References 45 publications

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“…3D): yield estimation (51.58%), phenotyping (27.60%), livestock monitoring (10.85%), insect monitoring (6.78%) and others (3.19%). The category 'others' includes applications related to the field management of orchards, such as pruning [43,44], path planning [45], plant water status [46], monitoring fruit ripeness [47] and monitoring resources [48,49].…”

Section: Literature Review Resultsmentioning

confidence: 99%

Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review

Farjon¹,

Liu²,

Edan³

2023

Preprint

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The number of objects is considered an important factor in a variety of tasks in the agricultural domain. Automated counting can improve farmers' decisions regarding yield estimation, stress detection, disease prevention, and more. In recent years, deep learning has been increasingly applied to many agriculture-related applications, complementing conventional computer-vision algorithms for counting agricultural objects. This article reviews progress in the past decade and the state of the art for counting methods in agriculture, focusing on deep-learning methods. It presents an overview of counting algorithms, metrics, platforms, and sensors, a list of all publicly available datasets, and an in-depth discussion of various deep-learning methods used for counting. Finally, it discusses open challenges in object counting using deep learning and gives a glimpse into new directions and future perspectives for counting research. The review reveals a major leap forward in object counting in agriculture in the past decade, led by the penetration of deep learning methods into counting platforms.

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Section: Literature Review Resultsmentioning

confidence: 99%

Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review

Farjon¹,

Liu²,

Edan³

2023

Preprint

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“…Tenorio et al use an RGB camera coupled to a pipe rail trolley to automatically collect images of cherry tomato clusters, where the distance between the camera and the fruits is around 1 m [ 33 ]. Then, the MobileNetV1 CNN is used to detect cherry tomato clusters, with an accuracy of 95.98%, after that, the improved color space segmentation works better for red tomato clusters, while not so accurate for some mixed and green clusters.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Detection and Segmentation of Mature Green Tomatoes Based on Mask R-CNN with Automatic Image Acquisition Approach

Zhao

Liu

et al. 2021

Sensors

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Since the mature green tomatoes have color similar to branches and leaves, some are shaded by branches and leaves, and overlapped by other tomatoes, the accurate detection and location of these tomatoes is rather difficult. This paper proposes to use the Mask R-CNN algorithm for the detection and segmentation of mature green tomatoes. A mobile robot is designed to collect images round-the-clock and with different conditions in the whole greenhouse, thus, to make sure the captured dataset are not only objects with the interest of users. After the training process, RestNet50-FPN is selected as the backbone network. Then, the feature map is trained through the region proposal network to generate the region of interest (ROI), and the ROIAlign bilinear interpolation is used to calculate the target region, such that the corresponding region in the feature map is pooled to a fixed size based on the position coordinates of the preselection box. Finally, the detection and segmentation of mature green tomatoes is realized by the parallel actions of ROI target categories, bounding box regression and mask. When the Intersection over Union is equal to 0.5, the performance of the trained model is the best. The experimental results show that the F1-Score of bounding box and mask region all achieve 92.0%. The image acquisition processes are fully unobservable, without any user preselection, which are a highly heterogenic mix, the selected Mask R-CNN algorithm could also accurately detect mature green tomatoes. The performance of this proposed model in a real greenhouse harvesting environment is also evaluated, thus facilitating the direct application in a tomato harvesting robot.

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“…Many of the surveyed articles demonstrate the application of DL models for crop yield estimation. The Single Shot MultiBox detector (SSD) method was used in the studies [ 37 , 43 , 51 , 53 ] to estimate tomato crops in the greenhouse environment followed by robotic harvesting. Other applications of SSD include detecting oyster mushrooms in [ 39 ] and sweet pepper in [ 49 ].…”

Section: Deep Learning In Ceamentioning

confidence: 99%

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

Ojo

Zahid

2022

Sensors

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Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop monitoring, detecting biotic and abiotic stresses, irrigation, microclimate prediction, energy efficient controls, and crop growth prediction. However, no review study assess DL’s state of the art to solve diverse problems in CEA. To fill this gap, we systematically reviewed DL methods applied to CEA. The review framework was established by following a series of inclusion and exclusion criteria. After extensive screening, we reviewed a total of 72 studies to extract the useful information. The key contributions of this article are the following: an overview of DL applications in different CEA facilities, including greenhouse, plant factory, and vertical farm, is presented. We found that majority of the studies are focused on DL applications in greenhouses (82%), with the primary application as yield estimation (31%) and growth monitoring (21%). We also analyzed commonly used DL models, evaluation parameters, and optimizers in CEA production. From the analysis, we found that convolutional neural network (CNN) is the most widely used DL model (79%), Adaptive Moment Estimation (Adam) is the widely used optimizer (53%), and accuracy is the widely used evaluation parameter (21%). Interestingly, all studies focused on DL for the microclimate of CEA used RMSE as a model evaluation parameter. In the end, we also discussed the current challenges and future research directions in this domain.

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Automatic visual estimation of tomato cluster maturity in plant rows

Cited by 9 publications

References 45 publications

Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review

Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review

Detection and Segmentation of Mature Green Tomatoes Based on Mask R-CNN with Automatic Image Acquisition Approach

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

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