Recognition of Areca Leaf Yellow Disease Based on PlanetScope Satellite Imagery

Areca yellow leaf disease is a major attacker of the planting and production of arecanut. The continuous expansion of arecanut (Areca catechu L.) planting areas in Hainan has placed a great need to strengthen the monitoring of this disease. At present, there is little research on the monitoring of areca yellow leaf disease. PlanetScope imagery can achieve daily global coverage at a high spatial resolution (3 m) and is thus suitable for the high-precision monitoring of plant pest and disease. In this paper, PlanetScope images were employed to extract spectral features commonly used in disease, pest and vegetation growth monitoring for primary models. In this paper, 13 spectral features commonly used in vegetation growth and pest monitoring were selected to form the initial feature space, followed by the implementation of the Correlation Analysis (CA) and independent t-testing to optimize the feature space. Then, the Random Forest (RF), Backward Propagation Neural Network (BPNN) and AdaBoost algorithms based on feature space optimization to construct double-classification (healthy, diseased) monitoring models for the areca yellow leaf disease. The results indicated that the green, blue and red bands, and plant senescence reflectance index (PSRI) and enhanced vegetation index (EVI) exhibited highly significant differences and strong correlations with healthy and diseased samples. The RF model exhibits the highest overall recognition accuracy for areca yellow leaf disease (88.24%), 2.95% and 20.59% higher than the BPNN and AdaBoost models, respectively. The commission and omission errors were lowest with the RF model for both healthy and diseased samples. This model also exhibited the highest Kappa coefficient at 0.765. Our results exhibit the feasible application of PlanetScope imagery for the regional large-scale monitoring of areca yellow leaf disease, with the RF method identified as the most suitable for this task. Our study provides a reference for the monitoring, a rapid assessment of the area affected and the management planning of the disease in the agricultural and forestry industries.

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Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors

Remote Sensing ◽

10.3390/rs13224562 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4562

Author(s):

Shuhan Lei ◽

Jianbiao Luo ◽

Xiaojun Tao ◽

Zixuan Qiu

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Plant Diseases ◽

Yellow Leaf ◽

Quantitative Expression ◽

Leaf Disease ◽

Sensing Technology ◽

Yellow Leaf Disease ◽

Qualitative Expression

Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). However, the correlation between the severity of the yellow leaf disease of arecanut and LVV must be demonstrated through research. Therefore, this study aims to apply the multispectral data obtained by the UAV along with the high-resolution UAV remote sensing images to obtain five vegetation indexes such as the normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), leaf chlorophyll index (LCI), green normalized difference vegetation index (GNDVI), and normalized difference red edge (NDRE) index, and establish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut, which is expressed by the proportion of the yellowing area of a single areca crown (in percentage). The traditional qualitative expression of this disease is transformed into the quantitative expression of the yellow leaf disease of arecanut per plant. The results demonstrate that the classification accuracy of the test set of the BPNN algorithm and SVM algorithm is the highest, at 86.57% and 86.30%, respectively. Additionally, the UAV structure from motion technology is used to measure the LVV of a single areca tree and establish a model of the correlation between the LVV and the severity of the yellow leaf disease of arecanut. The results show that the relative root mean square error is between 34.763% and 39.324%. This study presents the novel quantitative expression of the severity of the yellow leaf disease of arecanut, along with the correlation between the LVV of areca and the severity of the yellow leaf disease of arecanut. Significant development is expected in the degree of integration of multispectral software and hardware, observation accuracy, and ease of use of UAVs owing to the rapid progress of spectral sensing technology and the image processing and analysis algorithms.

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Incidence and detection of sugarcane yellow leaf disease in the Philippines

Australasian Plant Disease Notes ◽

10.1007/s13314-020-00412-w ◽

2020 ◽

Vol 15 (1) ◽

Author(s):

Jay-Vee S. Mendoza ◽

Marita S. Pinili ◽

Fe M. Dela Cueva

Keyword(s):

The Philippines ◽

Yellow Leaf ◽

Leaf Disease ◽

Yellow Leaf Disease

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Regeneration and evaluation of somaclones of sugarcane variety Co86032 for yellow leaf disease resistance and yield traits

Journal of Biosciences ◽

10.1007/s12038-019-9846-x ◽

2019 ◽

Vol 44 (2) ◽

Author(s):

Praveen Kona ◽

M Hemanth Kumar ◽

K H P Reddy ◽

T M Hemalatha ◽

D M Reddy ◽

...

Keyword(s):

Disease Resistance ◽

Yield Traits ◽

Yellow Leaf ◽

Sugarcane Variety ◽

Leaf Disease ◽

Yellow Leaf Disease

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Taqman quantitative PCR for detection of Indian arecanut yellow leaf disease phytoplasma

Phytopathogenic Mollicutes ◽

10.5958/2249-4677.2015.00070.5 ◽

2015 ◽

Vol 5 (2) ◽

pp. 113

Author(s):

Smita Nair ◽

Ramaswamy Manimekalai ◽

Soumya Vadakke Purayil ◽

Govind P. Rao

Keyword(s):

Quantitative Pcr ◽

Yellow Leaf ◽

Leaf Disease ◽

Yellow Leaf Disease

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An Investigation of Extreme Weather Impact on Precipitable Water Vapor and Vegetation Growth—A Case Study in Zhejiang China

Remote Sensing ◽

10.3390/rs13183576 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3576

Author(s):

Si Xiong ◽

Fei Guo ◽

Qingzhi Zhao ◽

Liangke Huang ◽

Lin He ◽

...

Keyword(s):

Water Vapor ◽

Extreme Precipitation ◽

Large Scale ◽

Vegetation Index ◽

Sunshine Duration ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Zhejiang Province ◽

Vegetation Growth ◽

Extreme Climate

Zhejiang province in China experienced an extreme climate phenomenon in August 2014 with temperature rises, sunshine duration decreases, and precipitation increases, particularly, the successive heavy rainfall events occurring from 16 to 20 August 2014 that contributed to this climate anomaly. This study investigates the spatial-temporal variation characteristics of precipitable water vapor (PWV) and the normalized difference vegetation index (NDVI) associated with this phenomenon. Multiple sources of PWV values derived from the Global Positioning System (GPS), Radiosonde (RS) and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim data are used with different spatiotemporal resolutions. The monthly averaged PWV in August 2014 exceeded the 95% percentiles of climatological value (53 mm) while the monthly averaged temperature was less than the 5% percentiles of climatological value (26.6 °C). Before the extreme precipitation, the PWV increased from the yearly averaged value of about 35 mm to more than 60 mm and gradually returned to the August climatological average of 50 mm after the precipitation ended. A large-scale atmospheric water vapor was partially conveyed by the warm wet air current of anticyclones which originated over the South China Sea (25° N, 130° E) and the Western Pacific Ocean. The monthly NDVI variation over the past 34 years (1982–2015) was investigated in this paper and the significant impact of extreme climate on vegetation growth in August 2014 was found. The extreme negative temperature anomaly and positive PWV anomaly are the major climate-driven factors affecting vegetation growth in the north and south of Zhejiang province with correlation coefficients of 0.83 and 0.72, respectively, while the extreme precipitation does not show any apparent impact on NDVI.

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