A Detection Method for Tomato Fruit Common Physiological Diseases Based on YOLOv2

Zhao, Jiayue; Qu, Jianhua

doi:10.1109/itme.2019.00132

Cited by 21 publications

(7 citation statements)

References 8 publications

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“…In their study, Zhao and Qu [21] propose techniques for identifying both sound tomato fruits and those affected by prevalent physiological disorders. The dataset underwent several improvements to optimize network performance: initially, the picture datasets were enriched with additional data to mitigate the risk of overfitting.…”

Section: Issn: 2302-9285 mentioning

confidence: 99%

A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector

Che Ani,

Hamdani Mohammad Farid,

Firdhaus Kamaruzaman

et al. 2024

Bulletin EEI

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The image datasets that are most widely used for training deep learning models are specifically developed for applications. This study introduces a novel dataset aimed at augmenting the existing data for the identification of figs in their natural habitats, specifically in the wilderness. In the present study, researchers have generated numerous image datasets specifically for object detection focus on applications in agriculture. Regrettably, it is exceedingly difficult for us to obtain a specialized dataset specifically designed for detecting figs. To tackle this issue, a grand total of 462 photographs of fig fruits were gathered. The augmentation technique was utilized to substantially increase the size of the dataset. Ultimately, we conduct an examination of the dataset by doing a baseline performance study for bounding-box detection using established object detection methods, specifically you only look once (YOLO) version 3 and YOLOv4. The performance obtained on the test photos of our dataset is satisfactory. For farmers, the capacity to identify and oversee fig fruits in their natural or developed environments can be highly advantageous. The detecting device offers instantaneous data regarding the quantity of mature figs, facilitating decision-making procedures.

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Section: Issn: 2302-9285 mentioning

confidence: 99%

A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector

Che Ani,

Hamdani Mohammad Farid,

Firdhaus Kamaruzaman

et al. 2024

Bulletin EEI

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“…Jiayue et al, [5] performed the recognition of tomato fruits with disease, the technique called YOLOv2 CNN was used. YOLOv2 is based on regression model and uses a target detection algorithm, which exhibits fast detection speed and good accuracy.…”

Section: Related Workmentioning

confidence: 99%

Plant Disease Detection and Classification using CNN

Rinu¹,

Manjula²

2021

IJRTE

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Agriculture is one field which has a high impact on life and economic status of human beings. Improper management leads to loss in agricultural products. Diseases are detrimental to the plant’s health which in turn affects its growth. To ensure minimal loss to the cultivated crop, it is crucial to supervise its growth. Convolutional Neural Network is a class of Deep learning used majorly for image classification, other mainstream tasks such as image segmentation and signal processing. The main aim of the proposed work is to find a solution to the problem of 38 different classes of plant diseases detection using the simplest approach while making use of minimal computing resources to achieve better results compared to the traditional models. VGG16 training model is deployed for detection and classification of plant diseases. Neural network models employ automatic feature extraction to aid in the classification of the input image into respective disease classes. This proposed system has achieved an average accuracy of 94.8% indicating the feasibility of the neural network approach even under unfavorable conditions.

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“…It adjusted the network structure and multiscale feature object detection method used and softmax utilized for object classification that counts. Therefore, many efforts were made to perform flower and fruit detection based on these algorithms of the YOLO detection method [23][24][25]. While its detection speed has been greatly improved compared with the two-stage detector, the positioning accuracy of it has been reduced, especially for some small objects.…”

Section: Flower Classification and Detection Based On Deepmentioning

confidence: 99%

Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device

Zhi-bin

Zhang

2020

Wireless Communications and Mobile Computing

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In this paper, a novel flower detection application anchor-based method is proposed, which is combined with an attention mechanism to detect the flowers in a smart garden in AIoT more accurately and fast. While many researchers have paid much attention to the flower classification in existing studies, the issue of flower detection has been largely overlooked. The problem we have outlined deals largely with the study of a new design and application of flower detection. Firstly, a new end-to-end flower detection anchor-based method is inserted into the architecture of the network to make it more precious and fast and the loss function and attention mechanism are introduced into our model to suppress unimportant features. Secondly, our flower detection algorithms can be integrated into the mobile device. It is revealed that our flower detection method is very considerable through a series of investigations carried out. The detection accuracy of our method is similar to that of the state-of-the-art, and the detection speed is faster at the same time. It makes a major contribution to flower detection in computer vision.

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A Detection Method for Tomato Fruit Common Physiological Diseases Based on YOLOv2

Cited by 21 publications

References 8 publications

A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector

A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector

Plant Disease Detection and Classification using CNN

Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device

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