Two-Stage Detection Algorithm for Kiwifruit Leaf Diseases Based on Deep Learning

Jia, Yinhua; Wang, Yübo; Xiang, Ying; Yang, Jia; Zhu, Yuhang; Li, Xin; Li, Shuangshuang; Zhang, Jie; Gong, Guoshu

doi:10.3390/plants11060768

Cited by 15 publications

(7 citation statements)

References 20 publications

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“…doi: 10.1108/JEDT-09-2021-0488 Thenmozhi, K., and Srinivasulu Reddy, U. (2019) Wang, Y., Zhu, Y., and Wang, Y. (2022).…”

Section: Discussionmentioning

confidence: 99%

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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“…doi: 10.1108/JEDT-09-2021-0488 Thenmozhi, K., and Srinivasulu Reddy, U. (2019) Wang, Y., Zhu, Y., and Wang, Y. (2022).…”

Section: Discussionmentioning

confidence: 99%

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

2024

Frontiers Research Topics

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“…The two-stage models can output better recognition results, but the one-stage models are able to achieve faster detection [37]. Faster R-CNN is a typical two-stage model.…”

Section: Fruit Detectio Nmentioning

confidence: 99%

Fruit ripeness identification using YOLOv8 model

Xiao,

Nguyen,

Yan

2023

Multimed Tools Appl

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Deep learning-based visual object detection is a fundamental aspect of computer vision. These models not only locate and classify multiple objects within an image, but they also identify bounding boxes. The focus of this paper's research work is to classify fruits as ripe or overripe using digital images. Our proposed model extracts visual features from fruit images and analyzes fruit peel characteristics to predict the fruit's class. We utilize our own datasets to train two "anchor-free" models: YOLOv8 and CenterNet, aiming to produce accurate predictions. The CenterNet network primarily incorporates ResNet-50 and employs the deconvolution module DeConv for feature map upsampling. The final three branches of convolutional neural networks are applied to predict the heatmap. The YOLOv8 model leverages CSP and C2f modules for lightweight processing. After analyzing and comparing the two models, we found that the C2f module of the YOLOv8 model significantly enhances classification results, achieving an impressive accuracy rate of 99.5%.

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“…Researchers have also proposed computer vision applications for Kiwi crop disease recognition that use the YoloX architecture for detection and achieve satisfactory results in their experiment [ 36 ]. However, one of the difficulties may be integrating several technologies within a single application.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Strawberry Farming Using Edge Computing and IoT

Cruz

Mafra

Teixeira

et al. 2022

Sensors

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Strawberries are sensitive fruits that are afflicted by various pests and diseases. Therefore, there is an intense use of agrochemicals and pesticides during production. Due to their sensitivity, temperatures or humidity at extreme levels can cause various damages to the plantation and to the quality of the fruit. To mitigate the problem, this study developed an edge technology capable of handling the collection, analysis, prediction, and detection of heterogeneous data in strawberry farming. The proposed IoT platform integrates various monitoring services into one common platform for digital farming. The system connects and manages Internet of Things (IoT) devices to analyze environmental and crop information. In addition, a computer vision model using Yolo v5 architecture searches for seven of the most common strawberry diseases in real time. This model supports efficient disease detection with 92% accuracy. Moreover, the system supports LoRa communication for transmitting data between the nodes at long distances. In addition, the IoT platform integrates machine learning capabilities for capturing outliers in collected data, ensuring reliable information for the user. All these technologies are unified to mitigate the disease problem and the environmental damage on the plantation. The proposed system is verified through implementation and tested on a strawberry farm, where the capabilities were analyzed and assessed.

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Two-Stage Detection Algorithm for Kiwifruit Leaf Diseases Based on Deep Learning

Cited by 15 publications

References 20 publications

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

Fruit ripeness identification using YOLOv8 model

Smart Strawberry Farming Using Edge Computing and IoT

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