Fruit detection and load estimation of an orange orchard using the YOLO models through simple approaches in different imaging and illumination conditions

Mirhaji, Hamzeh; Soleymani, Mohsen; Asakereh, Abbas; Mehdizadeh, Saman Abdanan

doi:10.1016/j.compag.2021.106533

Cited by 87 publications

(28 citation statements)

References 19 publications

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“…As a result, YOLOv4 has the best performance by getting a mAP value of 90.8%. The enormous performance produced by the YOLOv4 algorithm from previous research is the basis for selecting the YOLOv4 algorithm in this research [8].…”

Section: Resultsmentioning

confidence: 99%

Deep Learning Model for Real-Time Multi-Class Detection on Food Ingredients Using Yolov4 Algorithm

Shovin¹,

Yusnitasari²,

Oswari³

et al. 2022

Proceedings of the 6th Batusangkar International Conference, BIC 2021, 11 - 12 October, 2021, Batusangkar-West Sumatra, Indones

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The position of food ingredients that may be piled and the similarity in terms of the shape, color, and texture of food ingredients become challenges to build a deep learning model that can perform optimal detection task on food ingredients. Therefore, the food ingredients will be trained and detected using the YOLOv4 algorithm because of its good performance. The data augmentation techniques from YOLOv4 algorithm also applied in this research to improve the variation and amounts of the collected dataset. To make the training more efficient, this research utilizes transfer learning method to adopt knowledge from YOLOv4 pre-trained model. The approach used in this research successfully creates a deep learning model for real-time multi-class detection on food ingredients with a reasonably good performance. The model shows performance with mAP@0.50 value of 84.90% and an average IoU of 72.77%.

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Section: Resultsmentioning

confidence: 99%

Deep Learning Model for Real-Time Multi-Class Detection on Food Ingredients Using Yolov4 Algorithm

Shovin¹,

Yusnitasari²,

Oswari³

et al. 2022

Proceedings of the 6th Batusangkar International Conference, BIC 2021, 11 - 12 October, 2021, Batusangkar-West Sumatra, Indones

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“…Due to its much faster execution speed than Faster R-CNN, the YOLO model is continuously improved in both speed and accuracy of detection, with many versions such as YOLOv1, YOLOv2, YOLOv3, YOLOv4, YOLOv5 [27][28][29][30] and YOLOv7 [31]. Several studies have been conducted using Faster R-CNN [14,32,33] or YOLO [34][35][36][37][38] to detect fruits.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to Cashew Nut Detection in Packaging and Quality Inspection Lines

Pham¹,

Nguyen²,

Pham³

2022

IJACSA

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YOLO standing for You Only Look Once is one of the most famous algorithms in computer vision used for detecting objects in a real-time environment. The newest version of this algorithm, namely YOLO with the seventh version or YOLOv7, is proposed in the present study for cashew nut detection (good, broken and not peeled) in packaging and quality inspection lines. Furthermore, this algorithm using an efficient convolutional neural network (CNN) to be able to successfully detect and identify unsatisfactory cashew nuts, such as chipped or burnt cashews. In order to deal with the quality inspection process, a new dataset called CASHEW dataset has been built at first by collecting cashew images in environments with different brightness and camera angles to ensure the model's effectiveness. The quality inspection of cashew nuts is tested with a huge number of YOLOv7 models and their effectiveness will also be evaluated. The experimental results show that all models are able to obtain high accuracy. Among them, the YOLOv7-tiny model employs the least number of parameters, i.e. 6.2M but has many output parameters with higher accuracy than that of some other YOLO models. As a result, the proposed approach should clearly be one of the most feasible solutions for the cashew's quality inspection.

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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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Fruit detection and load estimation of an orange orchard using the YOLO models through simple approaches in different imaging and illumination conditions

Cited by 87 publications

References 19 publications

Deep Learning Model for Real-Time Multi-Class Detection on Food Ingredients Using Yolov4 Algorithm

Deep Learning Model for Real-Time Multi-Class Detection on Food Ingredients Using Yolov4 Algorithm

A Novel Approach to Cashew Nut Detection in Packaging and Quality Inspection Lines

Detection of coffee fruits on tree branches using computer vision

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