VMF-SSD: A Novel V-Space Based Multi-Scale Feature Fusion SSD for Apple Leaf Disease Detection

Liu, Tian; Zhang, Haixi; Liu, Bin; Zhang, Jiaqi; Duan, Nannan; Yuan, Aihong Yuan; Huo, Yingqiu

doi:10.1109/tcbb.2022.3229114

Cited by 7 publications

(1 citation statement)

References 33 publications

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“…With the widespread application of deep learning techniques [25][26][27][28] in agriculture, researchers used a series of object detection models, such as SSD [29], Faster R-CNN [30], and YOLO [31][32][33][34] to conduct crop detection research. Jingjing Tian [35] proposed an apple leaf disease detection method based on the SSD network, which improved the algorithm by establishing a multi-scale feature extractor, designing a V-space-assisted localization branch, and constructing a multi-scale attention mechanism module. The improved algorithm achieved a mAP of 83.42% for apple leaf disease detection.…”

Section: Introductionmentioning

confidence: 99%

Study on the Detection Method for Daylily Based on YOLOv5 under Complex Field Environments

Yan

Cai

et al. 2023

Plants

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Intelligent detection is vital for achieving the intelligent picking operation of daylily, but complex field environments pose challenges due to branch occlusion, overlapping plants, and uneven lighting. To address these challenges, this study selected an intelligent detection model based on YOLOv5s for daylily, the depth and width parameters of the YOLOv5s network were optimized, with Ghost, Transformer, and MobileNetv3 lightweight networks used to optimize the CSPDarknet backbone network of YOLOv5s, continuously improving the model’s performance. The experimental results show that the original YOLOv5s model increased mean average precision (mAP) by 49%, 44%, and 24.9% compared to YOLOv4, SSD, and Faster R-CNN models, optimizing the depth and width parameters of the network increased the mAP of the original YOLOv5s model by 7.7%, and the YOLOv5s model with Transformer as the backbone network increased the mAP by 0.2% and the inference speed by 69% compared to the model after network parameter optimization. The optimized YOLOv5s model provided precision, recall rate, mAP, and inference speed of 81.4%, 74.4%, 78.1%, and 93 frames per second (FPS), which can achieve accurate and fast detection of daylily in complex field environments. The research results can provide data and experimental references for developing intelligent picking equipment for daylily.

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