Monitoring the damage of armyworm as a pest in summer corn by unmanned aerial vehicle imaging

Tao, Wancheng; Wang, Xinsheng; Xue, Jing-Hao; Su, Wei; Zhang, Mingzheng; Yin, Dafei; Zhu, Ding; Xie, Zixuan; Zhang, Ying

doi:10.1002/ps.6852

Cited by 12 publications

(9 citation statements)

References 40 publications

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“…Moreover, it reduces overfitting and increases the precision. The work presented by Tao et al [14] which is based on multi-spectral UAV dataset and four machine learning algorithms, showed that the RF has the greatest potential with highest accuracy in terms of identifying the armyworm infected areas. Furthermore, the spatial distribution pattern of the monitoring results yielded by the three algorithms was evaluated and compared, as presented in Fig.…”

Section: Mapping Tsw Using Different Classification Algorithmsmentioning

confidence: 99%

“…de Oca et al [13] developed a low-cost unmanned aerial system for precision agriculture namely AgriQ, which is able to provide a series of vegetation indices based on a dual-spectrum system. Tao et al [14] applied a couple of machine learning methods including RF, Multilayer Perceptron (MLP), Naive Bayesian (NB) and SVM to monitor the damage of armyworm in summer corn based on the multispectral UAV data. Xavier et al [15] identified ramularia leaf blight cotton with an overall accuracy of 79% by using multispectral UAV imagery and four nonparametric classifiers, including multinomial logistic regression (MLR), multinomial logistic regression with boosting (MLRb), support vector machine (SVM), and random forest tree (RFT) for plant diseases and pests monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Thosea sinensis Walker in Tea Plantations Based on UAV Multi-Spectral Image

Yuan¹,

Yu²,

Zhang³

et al. 2023

Phyton

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Thosea sinensis Walker (TSW) rapidly spreads and severely damages the tea plants. Therefore, finding a reliable operational method for identifying the TSW-damaged areas via remote sensing has been a focus of a research community. Such methods also enable us to calculate the precise application of pesticides and prevent the subsequent spread of the pests. In this work, based on the unmanned aerial vehicle (UAV) platform, five band images of multispectral red-edge camera were obtained and used for monitoring the TSW in tea plantations. By combining the minimum redundancy maximum relevance (mRMR) with the selected spectral features, a comprehensive spectral selection strategy was proposed. Then, based on the selected spectral features, three classic machine learning algorithms, including random forest (RF), support vector machine (SVM), and k-nearest neighbors (KNN) were used to construct the pest monitoring model and were evaluated and compared. The results showed that the strategy proposed in this work obtained ideal monitoring accuracy by only using the combination of a few optimized features (2 or 4). In order to differentiate the healthy and TSW-damaged areas (2-class model), the monitoring accuracies of all the three models were computed, which were above 96%. The RF model used the least number of features, including only SAVI and Band red . In order to further discriminate the pest incidence levels (3-class model), the monitoring accuracies of all the three models were computed, which were above 80%, among which the RF algorithm based on SAVI, Band red , VARI_green, and Band red_edge features achieve the highest accuracy (OAA of 87%, and Kappa of 0.79). Considering the computational cost and model accuracy, this work recommends the RF model based on a few optimal feature combinations to monitor and distinguish the severity of TSW in tea plantations. According to the UAV remote sensing mapping results, the TSW infestation exhibited an aggregated distribution pattern. The spatial information of occurrence and severity can offer effective guidance for precise control of the pest. In addition, the relevant methods provide a reference for monitoring other leaf-eating pests, effectively improving the management level of plant protection in tea plantations, and guaranting the yield and quality of tea plantations.

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Section: Mapping Tsw Using Different Classification Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring Thosea sinensis Walker in Tea Plantations Based on UAV Multi-Spectral Image

Yuan¹,

Yu²,

Zhang³

et al. 2023

Phyton

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“…Nonlinear quantitative inversion models chiefly involve the utilization of machine learning classifiers to categorize and analyze pixel vectors. Commonly employed models include the maximum likelihood classifier (MLC) [19], support vector machine (SVM) [20], neural network [21], and logistic regression [22], among others, which obviate the necessity for feature extraction to derive classification outcomes. Among these machine learning algorithms, the MLC, grounded in Bayesian theory and augmented with prior knowledge fusion for classification, emerges as straightforward and user-friendly.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Thrips Damage Distribution in Mango Orchards Using a Novel Maximum Likelihood Classifier

Wang,

Tang,

Liu

et al. 2024

Agronomy

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Thrips constitute the primary pest responsible for reducing mango yield and quality every year in Asia. Therefore, the efficient monitoring of thrips damage across mango orchards on a large scale to aid farmers in devising rational pesticide application strategies poses a significant challenge within the current mango industry. This study designs a mango thrips damage inversion prediction method based on the maximum likelihood classifier (MLC). Initially, drone multispectral remote sensing technology is utilized to acquire multispectral data from mango orchards, which are then combined with ground hyperspectral information to identify sensitive bands indicative of mango leaf damage caused by thrips. Subsequently, correlation analysis is conducted on various vegetation indices, leading to the selection of the Greenness Normalized Difference Vegetation Index (GNDVI), which exhibits a strong correlation coefficient of 0.82, as the spectral characteristic parameter for the inversion prediction model. The construction of a remote sensing prediction model for thrips damage distribution in mango orchards is then undertaken based on the MLC. Acknowledging the bias-variance trade-off inherent in the MLC when processing spectral data and its potential limitations in feature extraction and robustness, this study proposes a modification wherein neighboring pixels are weighted differently to enhance the model’s feature extraction capabilities. Experimental results show that the novel MLC maintains stable estimation levels across various numbers of domain pixels, achieving an inversion accuracy of 91.23%. Through the reconstruction of the pixel matrix, the damage distribution of thrips in mango orchards can be swiftly and comprehensively visualized over extensive areas.

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“…Several factors affect maize productivity, such as water availability, seed quality, weather conditions, pests, diseases, soil management, and fertilizer nutrient content. Plant diseases [2] such as downy mildew and common rust, as well as pests such as corn armyworm [3][4][5] can significantly affect maize yields, leading to global supply and price fluctuations. Prediction of grain yield [6] is a critical process to estimate the amount of grain that a specific crop will produce.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Pukrongta,

Taparugssanagorn,

Sangpradit

2024

Preprint

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This paper presents the PEnsemble 4 model, a sophisticated machine learning framework that integrates IoT-based environmental data to accurately forecast maize yield. With the projected significant growth in global maize demand over the next decade, the inherent risks posed by the crop’s dependence on weather conditions necessitate improved prediction capabilities. The PEnsemble 4 model, developed with high accuracy, incorporates comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the PEnsemble 4 model effectively analyzes temporal patterns in vegetation indices, specifically CIre and NDRE, which serve as reliable indicators of canopy density and plant height. In addition, this research significantly contributes to precision agriculture by offering an efficient and sustainable alternative to conventional farming practices through precise yield predictions. Notably, the PEnsemble 4 model enables earlier estimation, advancing the timeline for yield prediction from the conventional day 100 in the R6 stage to day 79 in the R2 stage. This improvement enhances decision-making processes in farming operations. The remarkable accuracy rate of 91% underscores the importance of adopting a multifaceted data approach that harnesses IoT-derived environmental insights. Additionally, the PEnsemble 4 model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. Ultimately, the PEnsemble 4 model establishes a new standard in maize yield prediction, revolutionizing crop management and protection through the synergistic utilization of IoT and machine learning technologies.

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Monitoring the damage of armyworm as a pest in summer corn by unmanned aerial vehicle imaging

Cited by 12 publications

References 40 publications

Monitoring Thosea sinensis Walker in Tea Plantations Based on UAV Multi-Spectral Image

Monitoring Thosea sinensis Walker in Tea Plantations Based on UAV Multi-Spectral Image

Prediction of Thrips Damage Distribution in Mango Orchards Using a Novel Maximum Likelihood Classifier

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

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