The Gray Mold Spore Detection of Cucumber Based on Microscopic Image and Deep Learning

Abstract

Rapid and accurate detection of pathogen spores is an important step to achieve early diagnosis of diseases in precision agriculture. Traditional detection methods are time-consuming, laborious, and subjective, and image processing methods mainly rely on manually designed features that are difficult to cope with pathogen spore detection in complex scenes. Therefore, an MG-YOLO detection algorithm ( M ulti-head self-attention and G host-optimized YOLO ) is proposed to detect gray mold spores rapidly. Firstly, Multi-head self-attention is introduced in the backbone to capture the global information of the pathogen spores. Secondly, we combine weighted Bidirectional Feature Pyramid Network (BiFPN) to fuse multiscale features of different layers. Then, a lightweight network is used to construct GhostCSP to optimize the neck part. Cucumber gray mold spores are used as the study object. The experimental results show that the improved MG-YOLO model achieves an accuracy of 0.983 for detecting gray mold spores and takes 0.009 s per image, which is significantly better than the state-of-the-art model. The visualization of the detection results shows that MG-YOLO effectively solves the detection of spores in blurred, small targets, multimorphology, and high-density scenes. Meanwhile, compared with the YOLOv5 model, the detection accuracy of the improved model is improved by 6.8%. It can meet the demand for high-precision detection of spores and provides a novel method to enhance the objectivity of pathogen spore detection.

A Novel Crop Pest Detection Model Based on YOLOv5

“…To validate the detection performance of YOLOv5s-pest, we compare it with several methods, such as YOLOv5s-transfromer, YOLOv5s-bifpn, YOLOv5m-ghost from the hub of YOLOv5, YOLOv5-Lite-g from version 1.4 of YOLOv5-Lite [42], SE-YOLOv5s [43], MG-YOLO [44], YOLOv7-tiny [45], YOLOXs [46], and YOLOv8s [47]. Ensuring a fair comparison, we ensure that all models have similar computational complexity.…”

A Multiscale Point-Supervised Network for Counting Maize Tassels in the Wild

“…Soft attention involves calculating a weighted average of N inputs, while hard attention involves selecting only one input sequence at a specific position in the column, often based on the highest probability or randomly. Soft attention mechanisms are more widely used in CNNs [ 38 , 39 ] and can be further divided into the spatial, channel, and mixed domain attention mechanisms.…”

A deep semantic network-based image segmentation of soybean rust pathogens

“…Xie et al (2021) used Mask Scoring R-CNN network to detect mango disease spores to control and prevent mango disease. Li et al (2023) proposed an MG-YOLO detection algorithm that introduces Multi-head self-attention in the YOLO backbone and optimizes the network neck and pyramid structure for fast and accurate gray mold spores detection, with a detection accuracy of 0.983 for the improved model and a time spent of 0.009 seconds per image. Zhang et al (2023) introduced the attention mechanism module (ECA-Net) and adaptive feature fusion mechanism (ASFF) into the feature pyramid structure of YOLO to detect Fusarium germinate spores of small targets, and the average recognition accuracy of this model was 98.57%.…”

“…(2021) used Mask Scoring R-CNN network to detect mango disease spores to control and prevent mango disease. Li et al. (2023) proposed an MG-YOLO detection algorithm that introduces Multi-head self-attention in the YOLO backbone and optimizes the network neck and pyramid structure for fast and accurate gray mold spores detection, with a detection accuracy of 0.983 for the improved model and a time spent of 0.009 seconds per image.…”