Research on farmland crop classification based on UAV multispectral remote sensing images

Yang, Dongjian; Zhao, Jing; Lan, Yubin; Wen, Yu‐Ting; Pan, Fangjiang; Cao, Dianlong; Hu, Chuanxu; Guo, Jinkai

doi:10.33440/j.ijpaa.20200401.153

Cited by 6 publications

(6 citation statements)

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“…In recent years, leisure agriculture and rural tourism have gradually demonstrated the advantages of their sunrise industries. Not only have their scales expanded, but they have also driven other industries due to the promotion of markets and policies [4].…”

Section: Introductionmentioning

confidence: 99%

Change Detection Trend Prediction of Leisure Agriculture and Rural Tourism Based on High-Resolution Remote Sensing Image Classification Algorithm

2023

International Journal of Science and Engineering Applications

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Domain adaptation algorithms for source and target remote sensing images from the same region are studied. Remote sensing images in the same area are often less different and have strong correlations. This paper analyzes the ideas of getting rich in leisure agriculture and rural tourism under the development of the rural revitalization strategy, and proposes ways to revitalize the countryside and boost the economy, such as accelerating agricultural vocational education and changing the thinking of rural population management. A distance-based local support vector machine algorithm (DLSVM) is proposed. The algorithm compares the distance between the unlabeled sample and the hyperplane with a preset distance threshold. The ultimate goal of implementing the rural revitalization strategy is to continuously improve the participation and benefit of rural residents in industrial development, and completely solve rural problems. Industry and peasant employment issues.

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Section: Introductionmentioning

confidence: 99%

Change Detection Trend Prediction of Leisure Agriculture and Rural Tourism Based on High-Resolution Remote Sensing Image Classification Algorithm

2023

International Journal of Science and Engineering Applications

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“…Furthermore, UAVs can be equipped with sensors carrying enhanced spectral bands to reduce the spectral mixing of vegetation types during classification [17]. Such capabilities of UAVs was, for instance, exploited by Yang, et al [18] in the classification of green vegetation, including weeds, maize, and peach trees.…”

Section: Introductionmentioning

confidence: 99%

“…Weed detection requires the exploration of using remote sensing techniques suitable to optimally and accurately identify weed distribution in crops. High spatial resolution platforms have the ability to improve weed detection [15][16][17][18]. The spectral data obtained from these systems provide valuable information for discrimination between weeds and crops.…”

Section: Introductionmentioning

confidence: 99%

Weed Detection in Rainfed Maize Crops Using UAV and PlanetScope Imagery

de Villiers,

Munghemezulu,

Mashaba-Munghemezulu

et al. 2023

Sustainability

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Weed invasion of crop fields, such as maize, is a major threat leading to yield reductions or crop right-offs for smallholder farming, especially in developing countries. A synoptic view and timeous detection of weed invasions can save the crop. The sustainable development goals (SDGs) have identified food security as a major focus point. The objectives of this study are to: (1) assess the precision of mapping maize-weed infestations using multi-temporal, unmanned aerial vehicle (UAV), and PlanetScope data by utilizing machine learning algorithms, and (2) determine the optimal timing during the maize growing season for effective weed detection. UAV and PlanetScope satellite imagery were used to map weeds using machine learning algorithms—random forest (RF) and support vector machine (SVM). The input features included spectral bands, color space channels, and various vegetation indices derived from the datasets. Furthermore, principal component analysis (PCA) was used to produce principal components (PCs) that served as inputs for the classification. In this study, eight experiments are conducted, four experiments each for UAV and PlanetScope datasets spanning four months. Experiment 1 utilized all bands with the RF classifier, experiment 2 used all bands with SVM, experiment 3 employed PCs with RF, and experiment 4 utilized PCs with SVM. The results reveal that PlanetScope achieves accuracies below 49% in all four experiments. The best overall performance was observed for experiment 1 using the UAV based on the highest mean accuracy score (>0.88), which included the overall accuracy, precision, recall, F1 score, and cross-validation scores. The findings highlight the critical role of spectral information, color spaces, and vegetation indices in accurately identifying weeds during the mid-to-late stages of maize crop growth, with the higher spatial resolution of UAV exhibiting a higher precision in the classification accuracy than the PlanetScope imagery. The most optimal stage for weed detection was found to be during the reproductive stage of the crop cycle based on the best F1 scores being indicated for the maize and weeds class. This study provides pivotal information about the spatial distribution of weeds in maize fields and this information is essential for sustainable weed management in agricultural activities.

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“…At present, high-resolution (HR) remote sensing images have been widely used in many fields to obtain spatial information [7][8][9] , which provide more accurate information source for vegetation information monitoring. The use of remote sensing images is more convenient for extracting and monitoring vegetation information in the field scale [10][11][12] . Common FVC extraction mainly includes vegetation index threshold method, linear spectral mixed mode, machine learning and regression model [13] .…”

Section: Introduction mentioning

confidence: 99%

New method for cotton fractional vegetation cover extraction based on UAV RGB images

Yang

Lan

et al. 2022

International Journal of Agricultural and Biological Engineering

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As the key principle of precision farming, the distribution of fractional vegetation cover is the basis of crop management within the field serves. To estimate crop FVC rapidly at the farm scale, high temporal-spatial resolution imagery obtained by unmanned aerial vehicle (UAV) was adopted. To verify the application potential of consumer-grade UAV RGB imagery in estimated FVC, blue-green characteristic vegetation index (TBVI) and red-green vegetation index (TRVI) were proposed in this study according to the differences of the gray value among cotton vegetation, soil and shadow in the field. First, two new constructed indices and several published indices were used to extract visible light images and generate greyscale images for each of the visible light vegetation indices. Then, the thresholds of cotton vegetation and non-vegetation pixels were established based on the vegetation index threshold method which combines support vector machine classification and vegetation index. Finally, the accuracy difference in vegetation information extraction between the newly constructed and several published indices was compared. The results show that the accuracy of the information extracted by TRVI is higher than that of subdivision index of other visible light (FVC extraction precision in the first bud stage of cotton:

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Research on farmland crop classification based on UAV multispectral remote sensing images

Cited by 6 publications

References 0 publications

Change Detection Trend Prediction of Leisure Agriculture and Rural Tourism Based on High-Resolution Remote Sensing Image Classification Algorithm

Change Detection Trend Prediction of Leisure Agriculture and Rural Tourism Based on High-Resolution Remote Sensing Image Classification Algorithm

Weed Detection in Rainfed Maize Crops Using UAV and PlanetScope Imagery

New method for cotton fractional vegetation cover extraction based on UAV RGB images

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