Accurate modeling of vertical leaf nitrogen distribution in summer maize using in situ leaf spectroscopy via CWT and PLS-based approaches

Li, Lantao; Geng, Shaoqing; Dong, Lin; Su, Guangli; Zhang, Yinjie; Chang, Luyi; Ji, Yanru; Wang, Yilun; Wang, Lei

doi:10.1016/j.eja.2022.126607

Cited by 16 publications

(8 citation statements)

References 76 publications

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“…(4) To enhance model generalization and accuracy, we propose using AR to train the model with adversarial perturbations, improving the model's robustness to minor variations in input data and obtaining a more robust model. (5) Extensive experiments on the TLDITRD dataset confirm that our proposed LAFANet achieves accuracies of 81.7%, 83.3%, and 90.0% for image-to-text retrieval and 80.3%, 93.7%, and 96.3% for text-to-image retrieval, respectively. LAFANet surpasses the baseline models for FNE, advanced VSE∞, and NAAF techniques, boosting R@1 accuracy by 2.4%, 4.6%, and 4.3%, respectively, for image-to-text retrieval, and by 2.3%, 3.0%, and 3.2% for text-to-image retrieval tasks.…”

Section: Discussionmentioning

confidence: 64%

“…However, manual methods suffer from drawbacks such as high workload, low retrieval efficiency, inconsistent retrieval quality, and reliance on subjective judgment. In the agricultural domain, with the maturation of deep learning technology, complex single-modal management of tomato leaf diseases has been achieved [3][4][5][6][7][8][9][10][11]. Recently, with the diversification of agricultural data types [12], scholars are increasingly inclined towards employing cross-modal information retrieval techniques through various modalities, which provides valuable insights for our research.…”

Section: Introductionmentioning

confidence: 99%

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High-Accuracy Tomato Leaf Disease Image-Text Retrieval Method Utilizing LAFANet

Xu,

Zhou,

et al. 2024

Plants

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Tomato leaf disease control in the field of smart agriculture urgently requires attention and reinforcement. This paper proposes a method called LAFANet for image-text retrieval, which integrates image and text information for joint analysis of multimodal data, helping agricultural practitioners to provide more comprehensive and in-depth diagnostic evidence to ensure the quality and yield of tomatoes. First, we focus on six common tomato leaf disease images and text descriptions, creating a Tomato Leaf Disease Image-Text Retrieval Dataset (TLDITRD), introducing image-text retrieval into the field of tomato leaf disease retrieval. Then, utilizing ViT and BERT models, we extract detailed image features and sequences of textual features, incorporating contextual information from image-text pairs. To address errors in image-text retrieval caused by complex backgrounds, we propose Learnable Fusion Attention (LFA) to amplify the fusion of textual and image features, thereby extracting substantial semantic insights from both modalities. To delve further into the semantic connections across various modalities, we propose a False Negative Elimination-Adversarial Negative Selection (FNE-ANS) approach. This method aims to identify adversarial negative instances that specifically target false negatives within the triplet function, thereby imposing constraints on the model. To bolster the model’s capacity for generalization and precision, we propose Adversarial Regularization (AR). This approach involves incorporating adversarial perturbations during model training, thereby fortifying its resilience and adaptability to slight variations in input data. Experimental results show that, compared with existing ultramodern models, LAFANet outperformed existing models on TLDITRD dataset, with top1, top5, and top10 reaching 83.3% and 90.0%, and top1, top5, and top10 reaching 80.3%, 93.7%, and 96.3%. LAFANet offers fresh technical backing and algorithmic insights for the retrieval of tomato leaf disease through image-text correlation.

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Section: Discussionmentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%

High-Accuracy Tomato Leaf Disease Image-Text Retrieval Method Utilizing LAFANet

Xu,

Zhou,

et al. 2024

Plants

View full text Add to dashboard Cite

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“…First-order and second-order derivative processing are effective methods for resolving overlapping peaks and eliminating background noise [ 24 ]. CWT has the property of refining weak information to highlight localization and can effectively enhance spectrum feature information, and has been applied in noise reduction, de-contextualization, and compression of spectrum data [ 25 , 26 ]. The decomposition scales in CWT were set to 2,

, and

, which were chosen as the mother wavelet functions for the identification of the dopant content spectra.…”

Section: Methodsmentioning

confidence: 99%

“…The decomposition scales in CWT were set to 2,

, and

, which were chosen as the mother wavelet functions for the identification of the dopant content spectra. For simplification, the nine decomposition scales were designated as L1–L9, respectively [ 25 , 26 ].…”

Section: Methodsmentioning

confidence: 99%

Rapid and Low-Cost Quantification of Adulteration Content in Camellia Oil Utilizing UV-Vis-NIR Spectroscopy Combined with Feature Selection Methods

Liu

Gong

et al. 2023

Molecules

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This study aims to explore the potential use of low-cost ultraviolet-visible-near infrared (UV-Vis-NIR) spectroscopy to quantify adulteration content of soybean, rapeseed, corn and peanut oils in Camellia oil. To attain this aim, test oil samples were firstly prepared with different adulterant ratios ranging from 1% to 90% at varying intervals, and their spectra were collected by an in-house built experimental platform. Next, the spectra were preprocessed using Savitzky–Golay (SG)–Continuous Wavelet Transform (CWT) and the feature wavelengths were extracted using four different algorithms. Finally, Support Vector Regression (SVR) and Random Forest (RF) models were developed to rapidly predict adulteration content. The results indicated that SG–CWT with decomposition scale of 25 and the Iterative Variable Subset Optimization (IVSO) algorithm can effectively improve the accuracy of the models. Furthermore, the SVR model performed best for predicting adulteration of camellia oil with soybean oil, while the RF models were optimal for camellia oil adulterated with rapeseed, corn, or peanut oil. Additionally, we verified the models’ robustness by examining the correlation between the absorbance and adulteration content at certain feature wavelengths screened by IVSO. This study demonstrates the feasibility of using low-cost UV-Vis-NIR spectroscopy for the authentication of Camellia oil.

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“…Continuous wavelet transform (CWT) is a mathematical tool used for analyzing non-stationary signals, providing time-frequency representations of these signals by decomposing them into scaled and translated wavelets [19]. CWT convolves an input signal with wavelets at different scales and positions, measuring their similarities to the signal at each scale and time position throughout the convolution process.…”

Section: Introductionmentioning

confidence: 99%

Detection of Cotton Verticillium Wilt Disease Severity Based on Hyperspectrum and GWO-SVM

Zhang

Shang

et al. 2023

Remote Sensing

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In order to address the challenge of early detection of cotton verticillium wilt disease, naturally infected cotton plants in the field, which were divided into five categories based on the degree of disease severity, have been investigated in this study. Canopies of infected cotton plants were analyzed with spectral data measured, and various preprocessing techniques, including multiplicative scatter correction (MSC) and MSC-continuous wavelet analysis algorithms, were used to predict the disease severity. With a combination of support vector machine (SVM) models with such optimization algorithms as genetic algorithm (GA), grid search (GS), particle swarm optimization (PSO), and grey wolf optimizer (GWO), a grading model of cotton verticillium wilt disease was established in this study. The study results show that the MSC-PSO-SVM model outperforms the other three models in terms of classification accuracy, and the accuracy, macro precision, macro recall, and macro F1-score of this model are 80%, 81.26%, 80%, and 79.57%, respectively. Among those eight models constructed on the basis of continuous wavelet analyses using mexh and db3, the MSC-db3(23)-PSO-SVM and MSC-db3(23)-GWO-SVM models perform best, with the latter having a shorter running time. An overall evaluation shows that the MSC-db3(23)-GWO-SVM model is an optimal model, with values of its accuracy, macro precision, macro recall, and macro F1-score indicators being 91.2%, 92.02%, 91.2%, and 91.16%, respectively. Moreover, under this model, the prediction accuracy on disease levels 1 and 5 has achieved the highest rate of 100%, with a prediction accuracy rate of 88% on disease level 2 and the lowest prediction accuracy rate of 84% on both disease levels 3 and 4. These results demonstrate that it is effective to use spectral technology in classifying the cotton verticillium wilt disease and satisfying the needs of field detection and grading. This study provides a new approach for the detection and grading of cotton verticillium wilt disease and offered a theoretical basis for early prevention, precise drug application, and instrument development for the disease.

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Accurate modeling of vertical leaf nitrogen distribution in summer maize using in situ leaf spectroscopy via CWT and PLS-based approaches

Cited by 16 publications

References 76 publications

High-Accuracy Tomato Leaf Disease Image-Text Retrieval Method Utilizing LAFANet

High-Accuracy Tomato Leaf Disease Image-Text Retrieval Method Utilizing LAFANet

Rapid and Low-Cost Quantification of Adulteration Content in Camellia Oil Utilizing UV-Vis-NIR Spectroscopy Combined with Feature Selection Methods

Detection of Cotton Verticillium Wilt Disease Severity Based on Hyperspectrum and GWO-SVM

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