Effect of Different Plant Extracts on Olfactory Response of Bactrocera zonata Saunder and Postharvest Quality of Banana (Musa acuminata Colla)

Tajdar, Alia; Ejaz, Shaghef; Zaib, Muhammad Shah; Ishfaq, Anum; Zaka, Syed Muhammad; Safeer, Hafiz Muhammad; Abbas, Khalid

doi:10.17582/journal.pjz/20210716120712

Cited by 1 publication

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“…At present, two methods are mainly used for fruit target detection. One is the traditional image detection and segmentation technology that mainly uses color ( Janani and Jebakumar, 2023 ; Tajdar et al., 2023 ), texture ( Alshehhi and Marpu, 2023 ; Chapeta et al., 2023 ), edge ( Xie et al., 2022 ; Quan et al., 2023 ), and other feature information. However, the shallow features can only detect the target in a limited scene, and these methods often lack generalizability and robustness.…”

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confidence: 99%

Rapid detection of Yunnan Xiaomila based on lightweight YOLOv7 algorithm

et al. 2023

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IntroductionReal-time fruit detection is a prerequisite for using the Xiaomila pepper harvesting robot in the harvesting process.MethodsTo reduce the computational cost of the model and improve its accuracy in detecting dense distributions and occluded Xiaomila objects, this paper adopts YOLOv7-tiny as the transfer learning model for the field detection of Xiaomila, collects images of immature and mature Xiaomila fruits under different lighting conditions, and proposes an effective model called YOLOv7-PD. Firstly, the main feature extraction network is fused with deformable convolution by replacing the traditional convolution module in the YOLOv7-tiny main network and the ELAN module with deformable convolution, which reduces network parameters while improving the detection accuracy of multi-scale Xiaomila targets. Secondly, the SE (Squeeze-and-Excitation) attention mechanism is introduced into the reconstructed main feature extraction network to improve its ability to extract key features of Xiaomila in complex environments, realizing multi-scale Xiaomila fruit detection. The effectiveness of the proposed method is verified through ablation experiments under different lighting conditions and model comparison experiments.ResultsThe experimental results indicate that YOLOv7-PD achieves higher detection performance than other single-stage detection models. Through these improvements, YOLOv7-PD achieves a mAP (mean Average Precision) of 90.3%, which is 2.2%, 3.6%, and 5.5% higher than that of the original YOLOv7-tiny, YOLOv5s, and Mobilenetv3 models, respectively, the model size is reduced from 12.7 MB to 12.1 MB, and the model’s unit time computation is reduced from 13.1 GFlops to 10.3 GFlops.DiscussionThe results shows that compared to existing models, this model is more effective in detecting Xiaomila fruits in images, and the computational complexity of the model is smaller.

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