Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point

Wu, Fengyun; Duan, Jieli; Chen, Siyu; Ye, Yaxin; Ai, Puye; Yang, Zhou

doi:10.3389/fpls.2021.705021

Cited by 65 publications

(46 citation statements)

References 39 publications

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“…In recent years, the rapid development of machine vision and artificial intelligence has accelerated the process of engineering intelligence in various fields, and machine vision technology has also been rapidly improved in industrial, agricultural and other complex scene applications [4][5][6][7][8][9]. In response to the plant disease detection problem, disease detection methods based on visible light and near-infrared spectroscopic digital images have been widely used.…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Recognition Model Based on Improved YOLOv5

Wu²,

et al. 2022

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To accurately recognize plant diseases under complex natural conditions, an improved plant disease-recognition model based on the original YOLOv5 network model was established. First, a new InvolutionBottleneck module was used to reduce the numbers of parameters and calculations, and to capture long-distance information in the space. Second, an SE module was added to improve the sensitivity of the model to channel features. Finally, the loss function ‘Generalized Intersection over Union’ was changed to ‘Efficient Intersection over Union’ to address the former’s degeneration into ‘Intersection over Union’. These proposed methods were used to improve the target recognition effect of the network model. In the experimental phase, to verify the effectiveness of the model, sample images were randomly selected from the constructed rubber tree disease database to form training and test sets. The test results showed that the mean average precision of the improved YOLOv5 network reached 70%, which is 5.4% higher than that of the original YOLOv5 network. The precision values of this model for powdery mildew and anthracnose detection were 86.5% and 86.8%, respectively. The overall detection performance of the improved YOLOv5 network was significantly better compared with those of the original YOLOv5 and the YOLOX_nano network models. The improved model accurately identified plant diseases under natural conditions, and it provides a technical reference for the prevention and control of plant diseases.

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Section: Introductionmentioning

confidence: 99%

Plant Disease Recognition Model Based on Improved YOLOv5

Wu²,

et al. 2022

Self Cite

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“…YOLO series, which will be introduced in the following section, were used for cucumber internode length [32], kiwifruits [33], grapefruits [34], grapes [35], banana bunches [36], and banana bunches and stalks [37]. At the same time, improved networks based on YOLO series, namely MangoYOLO [38], YOLO-Tomato [39], and YOLOMuskmelon [40], were proposed; beyond these, the improved YOLOv3 was applied to detect the banana inflorescence axis [41] and an improved YOLOv5 method was described for apple detection [42].…”

Section: Introductionmentioning

confidence: 99%

YOLO-Banana: A Lightweight Neural Network for Rapid Detection of Banana Bunches and Stalks in the Natural Environment

Yang

et al. 2022

Agronomy

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The real-time detection of banana bunches and stalks in banana orchards is a key technology in the application of agricultural robots. The complex conditions of the orchard make accurate detection a difficult task, and the light weight of the deep learning network is an application trend. This study proposes and compares two improved YOLOv4 neural network detection models in a banana orchard. One is the YOLO-Banana detection model, which analyzes banana characteristics and network structure to prune the less important network layers; the other is the YOLO-Banana-l4 detection model, which, by adding a YOLO head layer to the pruned network structure, explores the impact of a four-scale prediction structure on the pruning network. The results show that YOLO-Banana and YOLO-Banana-l4 could reduce the network weight and shorten the detection time compared with YOLOv4. Furthermore, YOLO-Banana detection model has the best performance, with good detection accuracy for banana bunches and stalks in the natural environment. The average precision (AP) values of the YOLO-Banana detection model on banana bunches and stalks are 98.4% and 85.98%, and the mean average precision (mAP) of the detection model is 92.19%. The model weight is reduced from 244 to 137 MB, and the detection time is shortened from 44.96 to 35.33 ms. In short, the network is lightweight and has good real-time performance and application prospects in intelligent management and automatic harvesting in the banana orchard.

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“…Thus, analysis of HSI, such as classification, dimensionality reduction [1,5], and feature extraction [6,7], has obtained much attention among the remote sensing community for decades [8]. Moreover, such approaches can be applicable towards vision technology applications in other engineering domains [9][10][11], multispectral remote sensing, and synthetic aperture radar (SAR) imagery [12,13].…”

Section: Introductionmentioning

confidence: 99%

Enhanced TabNet: Attentive Interpretable Tabular Learning for Hyperspectral Image Classification

Shah

Xu³

2022

Remote Sensing

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Tree-based methods and deep neural networks (DNNs) have drawn much attention in the classification of images. Interpretable canonical deep tabular data learning architecture (TabNet) that combines the concept of tree-based techniques and DNNs can be used for hyperspectral image classification. Sequential attention is used in such architecture for choosing appropriate salient features at each decision step, which enables interpretability and efficient learning to increase learning capacity. In this paper, TabNet with spatial attention (TabNets) is proposed to include spatial information, in which a 2D convolution neural network (CNN) is incorporated inside an attentive transformer for spatial soft feature selection. In addition, spatial information is exploited by feature extraction in a pre-processing stage, where an adaptive texture smoothing method is used to construct a structure profile (SP), and the extracted SP is fed into TabNet (sTabNet) to further enhance performance. Moreover, the performance of TabNet-class approaches can be improved by introducing unsupervised pretraining. Overall accuracy for the unsupervised pretrained version of the proposed TabNets, i.e., uTabNets, can be improved from 11.29% to 12.61%, 3.6% to 7.67%, and 5.97% to 8.01% in comparison to other classification techniques, at the cost of increases in computational complexity by factors of 1.96 to 2.52, 2.03 to 3.45, and 2.67 to 5.52, respectively. Experimental results obtained on different hyperspectral datasets demonstrated the superiority of the proposed approaches in comparison with other state-of-the-art techniques including DNNs and decision tree variants.

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Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point

Cited by 65 publications

References 39 publications

Plant Disease Recognition Model Based on Improved YOLOv5

Plant Disease Recognition Model Based on Improved YOLOv5

YOLO-Banana: A Lightweight Neural Network for Rapid Detection of Banana Bunches and Stalks in the Natural Environment

Enhanced TabNet: Attentive Interpretable Tabular Learning for Hyperspectral Image Classification

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