Real-time detection of kiwifruit flower and bud simultaneously in orchard using YOLOv4 for robotic pollination

Guo, Li; Suo, Rui; Zhao, Guanao; Gao, Changqing; Fu, Longsheng; Shi, Fuxi; Dhupia, Jaspreet Singh; Li, Rui; Cui, Yongjie

doi:10.1016/j.compag.2021.106641

Cited by 60 publications

(22 citation statements)

References 22 publications

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“…The labelling boxes are rectangles surrounding the ROIs. The image annotation information of the flower clusters and flushes is saved in VOC format, and the corresponding XML files are generated after the labelling procedure ( Li et al, 2022 ; Lin Y. et al, 2022 ). The XML file contains the image storage information, image name, annotation name and coordinate information of each labelled rectangle box.…”

Section: Methodsmentioning

confidence: 99%

A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images

Lin

Jia

et al. 2022

Front. Plant Sci.

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Litchi flowering management is an important link in litchi orchard management. Statistical litchi flowering rate data can provide an important reference for regulating the number of litchi flowers and directly determining the quality and yield of litchi fruit. At present, the statistical work regarding litchi flowering rates requires considerable labour costs. Therefore, this study aims at the statistical litchi flowering rate task, and a combination of unmanned aerial vehicle (UAV) images and computer vision technology is proposed to count the numbers of litchi flower clusters and flushes in a complex natural environment to improve the efficiency of litchi flowering rate estimation. First, RGB images of litchi canopies at the flowering stage are collected by a UAV. After performing image preprocessing, a dataset is established, and two types of objects in the images, namely, flower clusters and flushes, are manually labelled. Second, by comparing the pretraining and testing results obtained when setting different training parameters for the YOLOv4 model, the optimal parameter combination is determined. The YOLOv4 model trained with the optimal combination of parameters tests best on the test set, at which time the mean average precision (mAP) is 87.87%. The detection time required for a single image is 0.043 s. Finally, aiming at the two kinds of targets (flower clusters and flushes) on 8 litchi trees in a real orchard, a model for estimating the numbers of flower clusters and flushes on a single litchi tree is constructed by matching the identified number of targets with the actual number of targets via equation fitting. Then, the data obtained from the manual counting process and the estimation model for the other five litchi trees in the real orchard are statistically analysed. The average error rate for the number of flower clusters is 4.20%, the average error rate for the number of flushes is 2.85%, and the average error for the flowering rate is 1.135%. The experimental results show that the proposed method is effective for estimating the litchi flowering rate and can provide guidance regarding the management of the flowering periods of litchi orchards.

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Section: Methodsmentioning

confidence: 99%

A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images

Lin

Jia

et al. 2022

Front. Plant Sci.

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“…Quan et al [ 24 ] used the YOLOv4 convolutional neural network to achieve accurate identification of corn seedlings and farmland weeds in complex field environments. Li et al [ 25 ] used the improved YOLOv4 to realize the recognition of kiwifruit flowers and buds in preparation for automatic machine pollination. Qi et al [ 26 ] proposed an improved YOLOv5 algorithm based on the attention mechanism to realize the detection of tomato pests and diseases.…”

Section: Introductionmentioning

confidence: 99%

Detection of Green Asparagus in Complex Environments Based on the Improved YOLOv5 Algorithm

Hong

et al. 2023

Sensors

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An improved YOLOv5 algorithm for the efficient recognition and detection of asparagus with a high accuracy in complex environments was proposed in this study to realize the intelligent machine harvesting of green asparagus. The coordinate attention (CA) mechanism was added to the backbone feature extraction network, which focused more attention on the growth characteristics of asparagus. In the neck part of the algorithm, PANet was replaced with BiFPN, which enhanced the feature propagation and reuse. At the same time, a dataset of asparagus in complex environments under different weather conditions was constructed, and the performance variations of the models with distinct attention mechanisms and feature fusion networks were compared through experiments. Experimental results showed that the mAP@0.5 of the improved YOLOv5 model increased by 4.22% and reached 98.69%, compared with the YOLOv5 prototype network. Thus, the improved YOLOv5 algorithm can effectively detect asparagus and provide technical support for intelligent machine harvesting of asparagus in different weather conditions and complex environments.

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“…For example, the YOLO (You Only Look Once) algorithm is a popular computer vision algorithm that has been used in several challenges in agriculture. YOLO has previously been used to detect flowers for robotic pollination (Li et al, 2022), fruit load and maturation (Cuong et al, 2022;Fu et al, 2022;Mirhaji et al, 2021), and weed detection (Parico and Ahamed, 2020). Therefore, this study aims to implement and explore different YOLO algorithms to detect coffee fruits on tree branches and classify the fruits according to the different maturation stages.…”

Section: Introductionmentioning

confidence: 99%

Detection of coffee fruits on tree branches using computer vision

Bazame

Molin

Althoff

et al. 2023

Sci. agric. (Piracicaba, Braz.)

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Coffee farmers do not have efficient tools to have sufficient and reliable information on the maturation stage of coffee fruits before harvest. In this study, we propose a computer vision system to detect and classify the Coffea arabica (L.) on tree branches in three classes: unripe (green), ripe (cherry), and overripe (dry). Based on deep learning algorithms, the computer vision model YOLO (You Only Look Once), was trained on 387 images taken from coffee branches using a smartphone. The YOLOv3 and YOLOv4, and their smaller versions (tiny), were assessed for fruit detection. The YOLOv4 and YOLOv4-tiny showed better performance when compared to YOLOv3, especially when smaller network sizes are considered. The mean average precision (mAP) for a network size of 800 × 800 pixels was equal to 81 %, 79 %, 78 %, and 77 % for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny, respectively. Despite the similar performance, the YOLOv4 feature extractor was more robust when images had greater object densities and for the detection of unripe fruits, which are generally more difficult to detect due to the color similarity to leaves in the background, partial occlusion by leaves and fruits, and lighting effects. This study shows the potential of computer vision systems based on deep learning to guide the decision-making of coffee farmers in more objective ways.

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Real-time detection of kiwifruit flower and bud simultaneously in orchard using YOLOv4 for robotic pollination

Cited by 60 publications

References 22 publications

A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images

A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images

Detection of Green Asparagus in Complex Environments Based on the Improved YOLOv5 Algorithm

Detection of coffee fruits on tree branches using computer vision

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