Muskmelon Maturity Stage Classification Model Based on CNN

Zhao, Huamin; Xu, Defang; Lawal, Olarewaju Mubashiru; Zhang, Shujuan

doi:10.1155/2021/8828340

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Several classifications of maturity levels of other agricultural commodities have also been applied [23][24][25]. Varur et al [23] classified coconut maturity levels by comparing several transfer learning architectures.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving System Accuracy by Modifying the Transfer Learning Architecture for Detecting Clove Maturity Levels

Rosihan,

Tempola,

Nurtanzis Sutoyo

et al. 2024

JAIT

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Detecting the maturity level of cloves is the initial stage in getting quality cloves. Early recognition of the maturity level of cloves is an essential stage in the clove industry. The maturity level of clove flowers can provide valuable information to clove farmers regarding clove harvest time. During the process of determining the level of maturity, it still relies on visual observation. This causes novice farmers and clove workers to still make mistakes in determining the start of the clove harvest. For this reason, in this research, initial detection of the maturity level of cloves was carried out based on images of clove flowers. There are four maturity levels: mature cloves, semimature cloves, overmature cloves, and dry cloves. The proposed research method is a modification of the transfer learning architecture. The research results show that modifying the Transfer learning architecture by adding three layers can increase system accuracy in the VGG16 and ResNet50 models by more than 5%, so that the highest accuracy obtained from modifying the VGG16 model is 95.5% and modifying the ResNet50 model is 87.75%. Meanwhile, for the VGG19 model, accuracy increased only when initializing the number of epochs to 10.

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Section: Literature Reviewmentioning

confidence: 99%

Improving System Accuracy by Modifying the Transfer Learning Architecture for Detecting Clove Maturity Levels

Rosihan,

Tempola,

Nurtanzis Sutoyo

et al. 2024

JAIT

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“…In recent years, researchers both domestically and internationally have made remarkable strides in leveraging deep learning techniques coupled with computer vision technology for agricultural applications [4][5][6]. You Only Look Once (YOLO) has garnered considerable attention as an advanced real-time target detection algorithm owing to its exceptional efficiency, speed, and accuracy [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Detection of Muskmelon Ripeness in Greenhouse Environment Based on YOLO-RFEW

Xu,

Ren,

Zhao

et al. 2024

Agronomy

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Accurate detection of muskmelon fruit ripeness is crucial to ensure fruit quality, optimize picking time, and enhance economic benefits. This study proposes an improved lightweight YOLO-RFEW model based on YOLOv8n, aiming to address the challenges of low efficiency in muskmelon fruit ripeness detection and the complexity of deploying a target detection model to a muskmelon picking robot. Firstly, the RFAConv replaces the Conv in the backbone part of YOLOv8n, allowing the network to focus more on regions with significant contributions in feature extraction. Secondly, the feature extraction and fusion capability are enhanced by improving the C2f module into a C2f-FE module based on FasterNet and an Efficient Multi-Scale attention (EMA) mechanism within the lightweight model. Finally, Weighted Intersection over Union (WIoU) is optimized as the loss function to improve target frame prediction capability and enhance target detection accuracy. The experimental results demonstrate that the YOLO-RFEW model achieves high accuracy, with precision, recall, F1 score, and mean Average Precision (mAP) values of 93.16%, 83.22%, 87.91%, and 90.82%, respectively. Moreover, it maintains a lightweight design and high efficiency with a model size of 4.75 MB and an inference time of 1.5 ms. Additionally, in the two types of maturity tests (M-u and M-r), APs of 87.70% and 93.94% are obtained, respectively, by the YOLO-RFEW model. Compared to YOLOv8n, significant improvements in detection accuracy have been achieved while reducing both model size and computational complexity using the proposed approach for muskmelon picking robots’ real-time detection requirements. Furthermore, when compared to lightweight models such as YOLOv3-Tiny, YOLOv4-Tiny, YOLOv5s, YOLOv7-Tiny, YOLOv8s, and YOLOv8n, the YOLO-RFEW model demonstrates superior performance with only 28.55%, 22.42%, 24.50%, 40.56%, 22.12%, and 79.83% of their respective model sizes, respectively, while achieving the highest F1 score and mAP values among these seven models. The feasibility and effectiveness of our improved scheme are verified through comparisons between thermograms generated by YOLOv8n and YOLO-RFEW as well as detection images. In summary, the YOLO-RFEW model not only improves the accuracy rate of muskmelon ripeness detection but also successfully realizes the lightweight and efficient performance, which has important theoretical support and application value in the field of muskmelon picking robot development.

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Framework for Lightweight Deep Learning Model Using YOLOv5 for Arecanut Grade Assessment

Naik,

Rudra

2024

SN COMPUT. SCI.

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Muskmelon Maturity Stage Classification Model Based on CNN

Cited by 5 publications

References 19 publications

Improving System Accuracy by Modifying the Transfer Learning Architecture for Detecting Clove Maturity Levels

Improving System Accuracy by Modifying the Transfer Learning Architecture for Detecting Clove Maturity Levels

Intelligent Detection of Muskmelon Ripeness in Greenhouse Environment Based on YOLO-RFEW

Framework for Lightweight Deep Learning Model Using YOLOv5 for Arecanut Grade Assessment

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