WITHDRAWN: Non-destructive fruit maturity detection methodology - A review

Arunkumar, M.; Rajendran, Anusha; Gunasri, S.; Kowsalya, M.; Krithika, C.K.

doi:10.1016/j.matpr.2020.12.1094

Cited by 12 publications

(5 citation statements)

References 43 publications

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“…It is fast, sensitive, safe, and offers direct internal characteristics measurement with wide frequency bandwidth( Meng et al, 2018 ; Ricci et al, 2022 ; Farina et al, 2018 ; Alsawaftah et al, 2022 ). Developing noninvasive imaging technology has been a focus of researchers to quickly and reliably image food to determine its characteristics ( Arunkumar et al, 2021 ). The features of various technologies for food imaging are summarized in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

Microwave imaging for watermelon maturity determination

Garvin

Abushakra

Choffin

et al. 2023

Current Research in Food Science

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Section: Introductionmentioning

confidence: 99%

Microwave imaging for watermelon maturity determination

Garvin

Abushakra

Choffin

et al. 2023

Current Research in Food Science

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“…Although they provide additional phenotypic data, their inherent delay in detection is a drawback. By contrast, nondestructive methods offer advantages such as reduced costs, heightened detection accuracy, and the ability to evaluate produce without damaging it (Arunkumar et al, 2021). Both domestic and international scholars have extensively investigated various nondestructive testing methods for fruits and nuts, including X-ray techniques, acoustic methods (Cobus and van Wijk, 2023), and machine vision approaches (Chakraborty et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Rapid and accurate detection of multi-target walnut appearance quality based on the lightweight improved YOLOv5s_AMM model

Zhan,

Li,

Lin

et al. 2023

Front. Plant Sci.

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IntroductionNut quality detection is of paramount importance in primary nut processing. When striving to maintain the imperatives of rapid, efficient, and accurate detection, the precision of identifying small-sized nuts can be substantially compromised.MethodsWe introduced an optimized iteration of the YOLOv5s model designed to swiftly and precisely identify both good and bad walnut nuts across multiple targets. The M3-Net network, which is a replacement for the original C3 network in MobileNetV3’s YOLOv5s, reduces the weight of the model. We explored the impact of incorporating the attention mechanism at various positions to enhance model performance. Furthermore, we introduced an attentional convolutional adaptive fusion module (Acmix) within the spatial pyramid pooling layer to improve feature extraction. In addition, we replaced the SiLU activation function in the original Conv module with MetaAconC from the CBM module to enhance feature detection in walnut images across different scales.ResultsIn comparative trials, the YOLOv5s_AMM model surpassed the standard detection networks, exhibiting an average detection accuracy (mAP) of 80.78%, an increase of 1.81%, while reducing the model size to 20.9 MB (a compression of 22.88%) and achieving a detection speed of 40.42 frames per second. In multi-target walnut detection across various scales, the enhanced model consistently outperformed its predecessor in terms of accuracy, model size, and detection speed. It notably improves the ability to detect multi-target walnut situations, both large and small, while maintaining the accuracy and efficiency.DiscussionThe results underscored the superiority of the YOLOv5s_AMM model, which achieved the highest average detection accuracy (mAP) of 80.78%, while boasting the smallest model size at 20.9 MB and the highest frame rate of 40.42 FPS. Our optimized network excels in the rapid, efficient, and accurate detection of mixed multi-target dry walnut quality, accommodating lightweight edge devices. This research provides valuable insights for the detection of multi-target good and bad walnuts during the walnut processing stage.

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“…The detection speed is slow, but more phenotypic information can be obtained. In contrast, non-destructive methods have the advantages of lower cost, more reliable detection results, and no need to destroy the fruit ( Arunkumar et al, 2021 ). Various non-destructive detecting methods for fruit maturity have been extensively studied, such as ultrasonic methods ( Yildiz et al, 2018 ), near-infrared spectroscopy ( Pissard et al, 2021 ), scanning laser Doppler vibrometers ( Hosoya et al, 2017 ), magnetic resonance imaging ( Srivastava et al, 2018 ), electronic nose ( Calvini and Pigani, 2022 ), and so on.…”

Section: Introductionmentioning

confidence: 99%

Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model

et al. 2022

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The fast and precise detection of dense litchi fruits and the determination of their maturity is of great practical significance for yield estimation in litchi orchards and robot harvesting. Factors such as complex growth environment, dense distribution, and random occlusion by leaves, branches, and other litchi fruits easily cause the predicted output based on computer vision deviate from the actual value. This study proposed a fast and precise litchi fruit detection method and application software based on an improved You Only Look Once version 5 (YOLOv5) model, which can be used for the detection and yield estimation of litchi in orchards. First, a dataset of litchi with different maturity levels was established. Second, the YOLOv5s model was chosen as a base version of the improved model. ShuffleNet v2 was used as the improved backbone network, and then the backbone network was fine-tuned to simplify the model structure. In the feature fusion stage, the CBAM module was introduced to further refine litchi’s effective feature information. Considering the characteristics of the small size of dense litchi fruits, the 1,280 × 1,280 was used as the improved model input size while we optimized the network structure. To evaluate the performance of the proposed method, we performed ablation experiments and compared it with other models on the test set. The results showed that the improved model’s mean average precision (mAP) presented a 3.5% improvement and 62.77% compression in model size compared with the original model. The improved model size is 5.1 MB, and the frame per second (FPS) is 78.13 frames/s at a confidence of 0.5. The model performs well in precision and robustness in different scenarios. In addition, we developed an Android application for litchi counting and yield estimation based on the improved model. It is known from the experiment that the correlation coefficient R2 between the application test and the actual results was 0.9879. In summary, our improved method achieves high precision, lightweight, and fast detection performance at large scales. The method can provide technical means for portable yield estimation and visual recognition of litchi harvesting robots.

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WITHDRAWN: Non-destructive fruit maturity detection methodology - A review

Cited by 12 publications

References 43 publications

Microwave imaging for watermelon maturity determination

Microwave imaging for watermelon maturity determination

Rapid and accurate detection of multi-target walnut appearance quality based on the lightweight improved YOLOv5s_AMM model

Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model

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