A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm

Mirzazadeh, Ali; Azizi, Afshin; Abbaspour‐Gilandeh, Yousef; Hernández-Hernández, José Luis; Hernández-Hernández, Mario; Gallardo-Bernal, Iván

doi:10.3390/agronomy11112364

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…Compared with the previous two oil tea cultivar recognition models, the overall accuracy and kappa coefficient of ResNet50 is higher, reaching 83.94% and 0.80. The ResNet50 also obtained better experimental results than the VGG16 model in the study of cultivar recognition of chrysanthemum and classification of rapeseed image [45,46]. This may be related to the greater number of hidden layers and the skip-connection structure in the ResNet50 model [47].However, for Ganshi 83-4, Changlin 53, Ganshi 84-8, and Gan 447, the InceptionV3, VGG16 and ResNet50 failed to recognize them accurately.…”

Section: Comparison and Analysis Of Cultivar Recognition Resultsmentioning

confidence: 97%

Bilinear Attention Network for Image-Based Fine-Grained Recognition of Oil Tea (Camellia oleifera Abel.) Cultivars

Zhu

Zheng

et al. 2022

Agronomy

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Oil tea (Camellia oleifera Abel.) is a high-quality woody oil crop unique to China and has extremely high economic value and ecological benefits. One problem in oil tea production and research is the worldwide confusion regarding oil tea cultivar nomenclature. The purpose of this study was to automatic recognize some oil tea cultivars using bilinear attention network. For this purpose, we explored this possibility utilizing the bilinear attention network for five common China cultivars Ganshi 83-4, Changlin 53, Changlin 3, Ganshi 84-8, and Gan 447. We adopted the bilinear EfficientNet-B0 network and the convolutional block attention module (CBAM) to build BA-EfficientNet model being able to automatically and accurately recognize oil tea cultivars. In addition, the InceptionV3, VGG16, and ResNet50 algorithms were compared with the proposed BA-EfficientNet. The comparative test results show that BA-EfficientNet can accurately recognize oil tea cultivars in the test set, with overall accuracy and kappa coefficients reaching 91.59% and 0.89, respectively. Compared with algorithms such as InceptionV3, VGG16, and ResNet50, the BA-EfficientNet algorithm has obvious advantages in most evaluation indicators used in the experiment. In addition, the ablation experiments were designed to quantitatively evaluate the specific effects of bilinear networks and CBAM modules on oil tea cultivar recognition results. The results demonstrate that BA-EfficientNet is useful for solving the problem of recognizing oil tea cultivars under natural conditions. This paper attempts to explore new thinking for the application of deep learning methods in the field of oil tea cultivar recognition under natural conditions.

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Section: Comparison and Analysis Of Cultivar Recognition Resultsmentioning

confidence: 97%

Bilinear Attention Network for Image-Based Fine-Grained Recognition of Oil Tea (Camellia oleifera Abel.) Cultivars

Zhu

Zheng

et al. 2022

Agronomy

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“…The amount of data in this experiment was also limited, and the training results are likely to exhibit overfitting. To solve this problem, we adopted the transfer learning method to improve model generalization [40]. We used the backbone of the COCO dataset to pre-train the network model and used the trained backbone to train the wildlife dataset.…”

Section: Pre-trainingmentioning

confidence: 99%

Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5

Namiki

Suzuki

et al. 2022

Applied Sciences

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With the development of science and technology, the traditional industrial structures are constantly being upgraded. As far as drones are concerned, an increasing number of researchers are using reinforcement learning or deep learning to make drones more intelligent. At present, there are many algorithms for object detection. Although many models have a high accuracy of detection, these models have many parameters and high complexity, making them unable to perform real-time detection. Therefore, it is particularly important to design a lightweight object detection algorithm that is able to meet the needs of real-time detection using UAVs. In response to the above problems, this paper establishes a dataset of six animals in grassland from different angles and during different time periods on the basis of the remote sensing images of drones. In addition, on the basis of the Yolov5s network model, a lightweight object detector is designed. First, Squeeze-and-Excitation Networks are introduced to improve the expressiveness of the network model. Secondly, the convolutional layer of branch 2 in the BottleNeckCSP structure is deleted, and 3/4 of its input channels are directly merged with the results of branch 1 processing, which reduces the number of model parameters. Next, in the SPP module of the network model, a 3 × 3 maximum pooling layer is added to improve the receptive field of the model. Finally, the trained model is applied to NVIDIA-TX2 processor for real-time object detection. After testing, the optimized YOLOv5 grassland animal detection model was able to effectively identify six different forms of grassland animal. Compared with the YOLOv3, EfficientDet-D0, YOLOv4 and YOLOv5s network models, the mAP_0.5 value was improved by 0.186, 0.03, 0.007 and 0.011, respectively, and the mAP_0.5:0.95 value was improved by 0.216, 0.066, 0.034 and 0.051, respectively, with an average detection speed of 26 fps. The experimental results show that the grassland animal detection model based on the YOLOv5 network has high detection accuracy, good robustness, and faster calculation speed in different time periods and at different viewing angles.

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“…In our opinion, such adaptive systems have a serious drawback, they cannot be customized to the individual characteristics of each root crop. In digital agriculture [10,11], computer vision systems are used to quickly detect and count plants [12][13][14][15], to determine their ripeness and diseases [16][17][18][19][20], as part of systems to protect against weeds and pests [21,22], to determine the position of cattle [23]. In recent years publications have shown that the problem of identifying diseased or mechanically damaged fetuses on transportation systems such as conveyor belts, drums, turbines and etc.…”

Section: Introductionmentioning

confidence: 99%

Identification and Classification of Mechanical Damage During Continuous Harvesting of Root Crops Using Computer Vision Methods

et al. 2022

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Detecting sugar beetroot crops with mechanical damage using machine learning methods is necessary for fine-tuning beet harvester units. The Agrifac HEXX TRAXX harvester with an installed computer vision system was investigated. A video camera (24 fps) was installed above the turbine, which receives the dug-out beets after the digger and is connected to a single-board computer. At the preprocessing stage, static and insignificant image details were revealed. Canny edge detector and excess green minus excess red (ExGR) method were used. The identified areas were excluded from the image. The remaining areas were glued with similar areas of another image. As a result, the number of images entering the second stage of preprocessing was reduced by half. Then Otsu's binarization was used. The main stage of image processing is divided into two sub-stages: detection and classification. The improved YOLOv4tiny method was chosen for root crop detection using a single-board computer (SBC). This method allows processing up to 14 images of 416 × 416 pixels with 86% precision and 91% recall. To classify root crop damage, we considered two algorithms as candidates: 1. bag of visual words (BoVW) with a support vector machine (SVM) classifier using histogram of oriented gradients (HOG) and scale-invariant feature transform (SIFT) descriptors; 2. convolutional neural networks (CNN). Under normal lighting conditions, CNN showed the best accuracy, which ranged from 97% to 100%, depending on the damage class. The implemented methods were used to detect and classify blurred images of sugar beetroots, which were previously rejected. For improved YOLOv4-tiny precision was 74% and recall was 70%. CNN classification accuracy ranged from 90% to 95% depending on the root crops damage class.

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A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm

Cited by 5 publications

References 39 publications

Bilinear Attention Network for Image-Based Fine-Grained Recognition of Oil Tea (Camellia oleifera Abel.) Cultivars

Bilinear Attention Network for Image-Based Fine-Grained Recognition of Oil Tea (Camellia oleifera Abel.) Cultivars

Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5

Identification and Classification of Mechanical Damage During Continuous Harvesting of Root Crops Using Computer Vision Methods

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