One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion

Abstract

Plant diseases such as drought stress and pest diseases significantly impact crops’ growth and yield levels. By detecting the surface characteristics of plant leaves, we can judge the growth state of plants and whether diseases occur. Traditional manual detection methods are limited by the professional knowledge and practical experience of operators. In recent years, a detection method based on deep learning has been applied to improve detection accuracy and reduce detection time. In this paper, we propose a disease detection method using a convolutional neural network (CNN) with multi-scale feature fusion for maize leaf disease detection. Based on the one-stage plant disease network YoLov5s, the coordinate attention (CA) attention module is added, along with a key feature weight to enhance the effective information of the feature map, and the spatial pyramid pooling (SSP) module is modified by data augmentation to reduce the loss of feature information. Three experiments are conducted under complex conditions such as overlapping occlusion, sparse distribution of detection targets, and similar textures and backgrounds of disease areas. The experimental results show that the average accuracy of the MFF-CNN is higher than that of currently used methods such as YoLov5s, Faster RCNN, CenterNet, and DETR, and the detection time is also reduced. The proposed method provides a feasible solution not only for the diagnosis of maize leaf diseases, but also for the detection of other plant diseases.

Attention-enhanced multiscale fusion for face mask-wearing detection

“…Object detection is one of the core topics in computer vision, which plays an important role in automatic driving, 9 object tracking, 10 image surveillance, 11 and remote sensing image analysis. 12 Krizhevsky et al 13 proposed the AlexNet network architecture, which is regarded as the pioneering convolutional neural network model in objective detection.…”

“…Object detection is one of the core topics in computer vision, which plays an important role in automatic driving, 9 object tracking, 10 image surveillance, 11 and remote sensing image analysis 12…”

Detection of Tip-Burn Stress on Lettuce Grown in an Indoor Environment Using Deep Learning Algorithms

“…Examples of the one-stage detector are SSD [ 17 ], YOLO [ 18 ], and CenterNet [ 19 ]. These one-stage detectors are not only able to reach high accuracies, but also have a faster processing speed [ 12 ], making them notable in the field of agriculture, where plant images are collected and utilized to classify plant species [ 20 , 21 , 22 ], count plants or fruits [ 23 , 24 ], identify pests [ 25 , 26 , 27 ] and weeds [ 28 , 29 ], and detect diseases [ 26 , 27 , 30 , 31 , 32 , 33 , 34 ].…”

Efficient attention-based CNN network (EANet) for multi-class maize crop disease classification

“…This approach showed an overall accuracy of 92.9% for categorizing six different maize diseases. Li et al (2022) presented an improved one-stage detection model i.e., YOLOv5 using multi-scale feature fusion for the detection of corn leaf infections. A spatial pyramid pooling and coordinate attention mechanism were introduced in the backbone network to improve the feature extraction and classification performance.…”

“…This approach showed an overall accuracy of 92.9% for categorizing six different maize diseases. Li et al. (2022) presented an improved one-stage detection model i.e., YOLOv5 using multi-scale feature fusion for the detection of corn leaf infections.…”