CBAM + ASFF-YOLOXs: An improved YOLOXs for guiding agronomic operation based on the identification of key growth stages of lettuce

Zhang, Pan; Li, Bingbing

doi:10.1016/j.compag.2022.107491

Cited by 11 publications

(5 citation statements)

References 37 publications

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“…used the CBAM module to assign more weight to disease information in grape leaf disease images, as well as less weight to background and noise, thus improving the ability to extract disease information. Zhang et al 36 . introduced the CBAM module in the YOLOXs network to ensure that the network can make full use of the valuable feature information of channel and spatial dimensions, thus improving the model's ability to identify key growth stages of lettuce.…”

Section: Methodsmentioning

confidence: 99%

“…Lin et al 35 used the CBAM module to assign more weight to disease information in grape leaf disease images, as well as less weight to background and noise, thus improving the ability to extract disease information. Zhang et al 36 introduced the CBAM module in the YOLOXs network to ensure that the network can make full use of the valuable feature information of channel and spatial dimensions, thus improving the model's ability to identify key growth stages of lettuce. In the present study, the CBAM is joined behind the Conv module to create the Conv_CBAM module, which is used to replace the Conv on the 18th layer of the network Neck part, as well as to enhance the attention of this layer to the leaf critical information of various diseases such as disease spots, morphology and color, improving the ability to detect the category and location of disease leaves, and further raising the balance of the identification effect of various diseases.…”

Section: Ms Fmentioning

confidence: 99%

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A tomato disease identification method based on leaf image automatic labeling algorithm and improved YOLOv5 model

Jing

Qiao

et al. 2023

J Sci Food Agric

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BACKGROUNDTomato is one of the most important vegetables in the world. Timely and accurate identification of tomato disease is a critical way to ensure the quality and yield of tomato production. The convolutional neural network is a crucial means of disease identification. However, this method requires manual annotation of a large amount of image data, which wastes the human cost of scientific research.RESULTSTo simplify the process of disease image labeling and improve the accuracy of tomato disease recognition and the balance of various disease recognition effects, a BC‐YOLOv5 tomato disease recognition method is proposed to identify healthy growth and nine types of diseased tomato leaves. In the present study, the YOLOv5 model is improved by designing an automatic tomato leaf image labeling algorithm, using the weighted bi‐directional feature pyramid network to change the Neck structure, adding the convolution block attention module, and changing the input channel of the detection layer. Experiments show that the BC‐YOLOv5 method has an excellent image annotation effect on tomato leaves, with a pass rate exceeding 95%. Furthermore, compared with existing models, the performance indices of BC‐YOLOv5 to identify tomato diseases are the best.CONCLUSIONBC‐YOLOv5 realizes the automatic labeling of tomato leaf images before the start of training. This method not only identifies nine common tomato diseases, but also improve the accuracy of disease identification and have a more balanced identification effect on various diseases. It provides a reliable method for the identification of tomato disease. © 2023 Society of Chemical Industry.

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Section: Methodsmentioning

confidence: 99%

Section: Ms Fmentioning

confidence: 99%

A tomato disease identification method based on leaf image automatic labeling algorithm and improved YOLOv5 model

Jing

Qiao

et al. 2023

J Sci Food Agric

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“…Their dataset consists of images mainly from the side view, focussed on flowers and fruits. The recognition of growth stages in hydroponic systems has been successfully implemented on lettuce using images from above only [23].…”

Section: Of 30mentioning

confidence: 99%

Detection of Growth Stages of Chilli Plants in a Hydroponic Grower Using Machine Vision and YOLOv8 Deep Learning Algorithms

Schneider,

Swiatek,

Jelali

2024

Preprint

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Vertical Indoor Farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use Deep Learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s eye and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering and fruiting classes. All trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.

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“…CBAM [29] is an attention mechanism that combines channel attention and spatial attention. As shown in Figure 6, kiwifruit feature maps are used as inputs to CBAM, which first undergoes channel attention to obtain the weights of each channel based on the similarity of the features in the feature maps, and the weights are weighted by a multiplier into the input feature layer.…”

Section: Feature Attention Enhancementmentioning

confidence: 99%

Efficient Non-Destructive Detection for External Defects of Kiwifruit

Wang,

Lv,

Pan

et al. 2023

Applied Sciences

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External defects of kiwifruit seriously affect its added commercialization. To address the existing problems, kiwifruit external defects detection has a few methods for detecting multi-category defects and weak adaptability to complex images. In this study, we proposed ResNet combined with CBAM for the automatic detection of external defects in kiwifruit. The experiment first built an acquisition device to obtain high-quality images. The optimal fusion scheme of ResNet and CBAM was investigated, the network training parameters were optimized, and Adam was used to accelerate the convergence speed of the model. It was found that the average recognition accuracy of ResNet34 + CBAM for kiwifruit was 99.6%, and all evaluation metrics were greater than 99%. Meanwhile, the experiment selected AlexNet, VGG16, InceptionV3, ResNet34, and ResNet34 + CBAM for comparison. The results showed that the recognition accuracy of ResNet34 + CBAM was 7.9%, 12.7%, 11.8%, and 4.3% higher than that of AlexNet, VGG16, InceptionV3, and ResNet34, respectively. Therefore, it can be concluded that ResNet34 + CBAM has the advantages of high recognition accuracy and good stability for kiwifruit external defect sample detection. It provides a technical guarantee for online detection and sorting of kiwifruit and other fruit defects.

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CBAM + ASFF-YOLOXs: An improved YOLOXs for guiding agronomic operation based on the identification of key growth stages of lettuce

Cited by 11 publications

References 37 publications

A tomato disease identification method based on leaf image automatic labeling algorithm and improved YOLOv5 model

A tomato disease identification method based on leaf image automatic labeling algorithm and improved YOLOv5 model

Detection of Growth Stages of Chilli Plants in a Hydroponic Grower Using Machine Vision and YOLOv8 Deep Learning Algorithms

Efficient Non-Destructive Detection for External Defects of Kiwifruit

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