Cotton stubble detection based on wavelet decomposition and texture features

Yang, Yukun; Nie, Jing; Zhang, Kan; Yang, Shuo; Zhao, Hangxing; Li, Jingbin

doi:10.1186/s13007-021-00809-3

Cited by 9 publications

(10 citation statements)

References 51 publications

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“…The most common gray level co-occurrence matrix (GLCM) algorithm was adopted to extract texture features (Yang et al, 2021 ). In this study, the energy (ENE), entropy (ENT), contrast (CON), correlation (COR), and their mean (MEA) and variance (VAR) in four directions were calculated by using the gray comatrix function.…”

Section: Methodsmentioning

confidence: 99%

Estimation Model of Potassium Content in Cotton Leaves Based on Wavelet Decomposition Spectra and Image Combination Features

Yao

Zhang

et al. 2022

Front. Plant Sci.

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Potassium (K) is one of the most important elements influencing cotton metabolism, quality, and yield. Due to the characteristics of strong fluidity and fast redistribution of the K in plants, it leads to rapid transformation of the K lack or abundance in plant leaves; therefore, rapid and accurate estimation of potassium content in leaves (LKC, %) is a necessary prerequisite to solve the regulation of plant potassium. In this study, we concentrated on the LKC of cotton in different growth stages, an estimation model based on the combined characteristics of wavelet decomposition spectra and image was proposed, and discussed the potential of different combined features in accurate estimation of the LKC. We collected hyperspectral imaging data of 60 main-stem leaves at the budding, flowering, and boll setting stages of cotton, respectively. The original spectrum (R) is decomposed by continuous wavelet transform (CWT). The competitive adaptive reweighted sampling (CARS) and random frog (RF) algorithms combined with partial least squares regression (PLSR) model were used to determine the optimal decomposition scale and characteristic wavelengths at three growth stages. Based on the best “CWT spectra” model, the grayscale image databases were constructed, and the image features were extracted by using color moment and gray level co-occurrence matrix (GLCM). The results showed that the best decomposition scales of the three growth stages were CWT-1, 3, and 9. The best growth stage for estimating LKC in cotton was the boll setting stage, with the feature combination of “CWT-9 spectra + texture,” and its determination coefficients (R2val) and root mean squared error (RMSEval) values were 0.90 and 0.20. Compared with the single R model (R2val = 0.66, RMSEval = 0.34), the R2val increased by 0.24. Different from our hypothesis, the combined feature based on “CWT spectra + color + texture” cannot significantly improve the estimation accuracy of the model, it means that the performance of the estimation model established with more feature information is not correspondingly better. Moreover, the texture features contributed more to the improvement of model performance than color features did. These results provide a reference for rapid and non-destructive monitoring of the LKC in cotton.

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Section: Methodsmentioning

confidence: 99%

Estimation Model of Potassium Content in Cotton Leaves Based on Wavelet Decomposition Spectra and Image Combination Features

Yao

Zhang

et al. 2022

Front. Plant Sci.

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“…GoogLeNet [ 20 ] is a new deep learning structure proposed by Christian Szegedy in 2014; all the deep learning structures before this one obtain better training results by increasing the depth (number of layers) of the network, but the increase in the number of layers brings many negative effects [ 15 ], such as overfitting, gradient disappearance, and gradient explosion. The proposal of GoogLeNet, on the other hand, improves the training results from the perspective of increasing the network width of the convolutional network in extracting deep features; the Inception structure is introduced to fuse feature information at different scales, as shown in Figure 2 .…”

Section: Googlenetmentioning

confidence: 99%

“…The advantages of GoogLeNet compared to other deep learning structures are [ 15 , 21 ]: it can use computational resources more efficiently and extract more features with the same amount of computation, thus improving the training results. The network structure of GoogLeNet is shown in Figure 3 .…”

Section: Googlenetmentioning

confidence: 99%

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Sound Recognition Method of Coal Mine Gas and Coal Dust Explosion Based on GoogLeNet

2023

Entropy

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To solve the problems of backward means of coal mine gas and coal dust explosion monitoring, late reporting, and low leakage rate, a sound recognition method of coal mine gas and coal dust explosion based on GoogLeNet was proposed. After installing mining pickups in key monitoring areas of coal mines to collect the sounds of the working equipment and the environment, the collected sound was analyzed by continuous wavelet to obtain its scale coefficient map. This was then imported into GoogLeNet to obtain the recognition model of coal mine gas and coal dust explosions. The test sound was obtained by continuous wavelet analysis to obtain the scale coefficient map, brought into the completed training recognition model to obtain the sound signal class, and verified by experiment. Firstly, the scale coefficient map extracted from the sound signal by continuous wavelet analysis showed that the similarity between the subjective and objective indicators of the wavelet coefficient maps of the gas explosion sound and coal dust explosion sound was higher, but the difference between these and the rest of the coal mine sounds was clearer, helping to effectively distinguish gas and coal dust explosion sounds from other sounds. Secondly, the experimental results of GoogLeNet parameters can be obtained. When the dropout parameter is 0.5 and the initial learning rate is 0.001, the recognition effect of the model established by GoogLeNet was optimal. According to the selected parameters, the training loss, testing loss, training recognition rate, and testing recognition rate of the model are all in line with expectations. Finally, the experimental recognition results show that the recognition rate of the proposed method is 97.38%, the recall rate is 86.1%, and the accuracy rate is 100% for the case of a 9:1 ratio of test data to training data, and the overall recognition effect of the proposed GoogLeNet is significantly better than that of vgg and Alexnet, which can effectively solve the problem of under-sampling of coal mine gas and coal dust explosion sounds and can meet the need for the intelligent recognition of coal mine gas and dust explosions.

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“…Another category is single‐stage algorithms, for example, You Only Look Once (YOLO) series algorithms [10, 11], single shot multibox detector (SSD) algorithms [12] etc. The representative models are: Tu Renwei et al.…”

Section: Introductionmentioning

confidence: 99%

STAE‐YOLO: Intelligent detection algorithm for risk management of construction machinery intrusion on transmission lines based on visual perception

Ji,

Zhang,

Huang

et al. 2023

IET Generation Trans & Dist

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The distribution of transmission lines is complex, influenced by terrain and topography, making their maintenance and management challenging. With the advancement of global mechanization, various types of construction machinery and large‐scale mechanical equipment are continually increasing. Accidental contact of construction machinery near transmission lines poses a serious threat to the stability of the power system. Traditional drones and inspection robots face difficulties in achieving real‐time monitoring of construction machinery and equipment around transmission lines, quickly identifying potential risks. Conventional image processing techniques and convolutional neural networks struggle with effective handling of small targets and densely packed detection tasks involving multiple targets. To address these challenges, this paper proposes an intelligent detection algorithm Swin Transformer attention efficient algorithm‐you only look once (YOLO) (STAE‐YOLO). It is based on the Swin Transformer global self‐attention mechanism, cross‐channel fusion attention mechanism, enhanced small object detection framework, and a focused and efficient regression loss function. The experimental results show that the STAE‐YOLO algorithm model improved by 6.3% in mean average precision, 3.7% in precision, and 3.1% in recall compared to the baseline model. Meanwhile, deploying the window multi‐head self‐attention global self‐attention mechanism in the model can strengthen the global multi‐scale semantic information in detection.

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Cotton stubble detection based on wavelet decomposition and texture features

Cited by 9 publications

References 51 publications

Estimation Model of Potassium Content in Cotton Leaves Based on Wavelet Decomposition Spectra and Image Combination Features

Estimation Model of Potassium Content in Cotton Leaves Based on Wavelet Decomposition Spectra and Image Combination Features

Sound Recognition Method of Coal Mine Gas and Coal Dust Explosion Based on GoogLeNet

STAE‐YOLO: Intelligent detection algorithm for risk management of construction machinery intrusion on transmission lines based on visual perception

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