Mapping Tobacco Fields Using UAV RGB Images

Zhu, Xiufang; Xiao, Guofeng; Ping, Wen; Zhang, Jinshui; Hou, Chenyao

doi:10.3390/s19081791

Cited by 11 publications

(5 citation statements)

References 30 publications

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“…Tobacco is one of the important commercial crops in several countries including Pakistan, India, China and the United States, with China being the largest producer of this crop [13]. In developing countries the estimation of crops is done manually [14].…”

Section: Related Workmentioning

confidence: 99%

“…Keeping in view the importance of this crop new paradigms of research in the detection, classification and field estimation are being carried out today. Zhu et alperfomed this task using RGB images taken using a UAV [13]. They have conducted experiments on tobacco field using only RGB images, which resulted in the overall accuracy of 95.93%.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

On the Performance of Temporal Stacking and Vegetation Indices for Detection and Estimation of Tobacco Crop

et al. 2020

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Machine learning in association with remote sensing has assisted agricultural specialists in monitoring, classification and yield estimation of crops. Tobacco is a major taxable crop of Pakistan, however the existing traditional methods for its monitoring and yield estimation are not only expensive and time consuming but also have limitations in terms of accuracy of collected data by a large number of diverse human surveyors. Due to the existence of such loopholes in the employed mechanism for tobacco crop monitoring and yield estimation, its illicit growth and distribution is on the rise. In this paper we have established a sophisticated machine learning mechanism for tobacco crop estimation using temporally stacked sentinel-2 satellite's data of Pakistan. Instead of the conventional approach of using single remotely sensed imagery for the target crop classification, we propose a machine learning based classification algorithm while keeping in view the phonological cycle of the target tobacco crop. Using the proposed mechanism, the temporal variations within the tobacco crop and its association with the variations of other vegetation is considered to improve the classification performance of the employed machine learning algorithm. Furthermore, the impact of stacking the vegetation indices derived from near infrared and vegetation red edge bands of sentinel-2 with the original sentinel-2 datasets, including Normalized Difference Vegetation Index (NDVI) and Normalized Difference Index 45 (NDI45), on the classification performance of the machine learning mechanism is investigated. Ground Truth data for training of our Artificial Neural Networks classifier, was obtained using indigenously developed survey application "GEOSurvey". Experiments were conducted using our proposed mechanism while considering various input data setups-including single date imagery, temporally stacked datasets based on phonological cycle of tobacco crop and different combinations of NDVI and NDI45 stacking. Our proposed experimental setup consisting of temporally stacked imagery along with NDVI stacking results in the best classification performance of 95.81% with reference to the single date imagery stacked with NDVI and NDI45, with performance gain of 07.32%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

On the Performance of Temporal Stacking and Vegetation Indices for Detection and Estimation of Tobacco Crop

et al. 2020

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“…The current method of extracting crop area mainly relies on remote sensing images, which include crop classification and field segmentation. Zhu et al proposed a method combining convolution and morphology to extract tobacco planting area in UAV remote sensing images with 95.93% accuracy [13]. After classifying the land cover from images, Fang et al calculated the crop area by cropping the research area following administrative boundaries [14].…”

Section: Introductionmentioning

confidence: 99%

Extraction of Tobacco Planting Information Based on UAV High-Resolution Remote Sensing Images

He,

Liao,

et al. 2024

Remote Sensing

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Tobacco is a critical cash crop in China, so its growing status has received more and more attention. How to acquire accurate plant area, row spacing, and plant spacing at the same time have been key points for its grow status monitoring and yield prediction. However, accurately detecting small and densely arranged tobacco plants during the rosette stage poses a significant challenge. In Sichuan Province, the contours of scattered tobacco fields with different shapes are not well-extracted. Additionally, there is a lack of simultaneous methods for extracting crucial tobacco planting information, including area, row spacing, and plant spacing. In view of the above scientific problems, we proposed a method to extract the planting information of tobacco at the rosette stage with Unmanned Aerial Vehicle (UAV) remote sensing images. A detection model, YOLOv8s-EFF, was constructed for the small and weak tobacco in the rosette stage. We proposed an extraction algorithm for tobacco field area based on extended contours for different-shaped fields. Meanwhile, a planting distance extraction algorithm based on tobacco coordinates was presented. Further, four experimental areas were selected in Sichuan Province, and image processing and sample label production were carried out. Four isolated tobacco fields with different shapes in four experimental areas were used to preliminarily verify the effectiveness of the model and algorithm proposed. The results show that the precision ranges of tobacco field area, row spacing, and plant spacing were 96.51~99.04%, 90.08~99.74%, and 94.69~99.15%, respectively. And another two experimental areas, Jiange County, Guangyuan, and Dazhai County, Gulin County, and Luzhou, were selected to evaluate the accuracy of the method proposed in the research in practical application. The results indicate that the average accuracy of tobacco field area, row spacing, and plant spacing extracted by this method reached 97.99%, 97.98%, and 98.31%, respectively, which proved the extraction method of plant information is valuable.

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“…Remote sensing data has the advantages of high spatial resolution, high timeliness, objectivity, and reliability. Moreover, they are widely used in crop plantation distribution extraction [8][9][10][11][12]. However, the data processing for long time series and large-scale remote sensing data is complicated both in terms of methods and computations.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Change in Maize Plantation Distribution and Its Driving Factors in Heilongjiang Province, China

et al. 2022

Self Cite

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Accurate identification of maize plantation distribution and timely examination of key spatial-temporal drivers is a practice that can support agricultural production estimates and development decisions. Previous studies have rarely used efficient cloud processing methods to extract crop distribution, and meteorological and socioeconomic factors were often considered independently in driving force analysis. In this paper, we extract the spatial distribution of maize using classification and regression tree (CART) and random forest (RF) algorithms based on the Google Earth Engine (GEE) platform. Combining remote sensing, meteorological and statistical data, the spatio-temporal variation characteristics of maize plantation proportion (MPP) at the county scale were analyzed using trend analysis, kernel density estimation, and standard deviation ellipse analysis, and the driving forces of MPP spatio-temporal variation were explored using partial correlation analysis and geodetectors. Our empirical results in Heilongjiang province, China showed that (1) the CART algorithm achieved higher classification accuracy than the RF algorithm; (2) MPP showed an upward trend in more than 75% of counties, especially in high-latitude regions; (3) the main climatic factor affecting the inter-annual fluctuation of MPP was relative humidity; (4) the impact of socioeconomic factors on MPP spatial distribution was significantly larger than meteorological factors, the temperature was the most important meteorological factor, and the number of rural households was the most important socioeconomic factor affecting MPP spatial distribution. The interaction between different factors was greater than a single factor alone; (5) the correlation between meteorological factors and MPP differed across different latitudinal regions and landforms. This research provides a key reference for the optimal adjustment of crop cultivation distribution and agricultural development planning and policy.

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Mapping Tobacco Fields Using UAV RGB Images

Cited by 11 publications

References 30 publications

On the Performance of Temporal Stacking and Vegetation Indices for Detection and Estimation of Tobacco Crop

On the Performance of Temporal Stacking and Vegetation Indices for Detection and Estimation of Tobacco Crop

Extraction of Tobacco Planting Information Based on UAV High-Resolution Remote Sensing Images

Analysis of Change in Maize Plantation Distribution and Its Driving Factors in Heilongjiang Province, China

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