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Goswami, Jonali
- Rapid Assessment of Boro Paddy Infestation by Brown Planthopper in Morigaon District, Assam, India Using Unmanned Aerial Vehicle
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PDF Views:91
Authors
Affiliations
1 North Eastern Space Applications Centre, Department of Space, Government of India, Umiam 793 103, IN
1 North Eastern Space Applications Centre, Department of Space, Government of India, Umiam 793 103, IN
Source
Current Science, Vol 111, No 10 (2016), Pagination: 1604-1606Abstract
In April 2016, farmers from Morigaon and Nagaon districts of Assam, India encountered severe pest infestation in their boro paddy (summer paddy) areas, which was unusual. Morigaon district with an area of 1550 sq. km having a population of about 9.6 lakhs (as of 2011), was the worst affected with four out of the five revenue circles, viz. Mayong, Bhuragaon, Laharighat, Morigaon and Mikirbheta being affected. Investigations by the District Agricultural Department and Regional Agricultural Research Station, Nagaon confirmed it to be infestation by brown planthopper (BPH), Nilaparvata lugens (Stal). Planthoppers are a problem in rainfed and irrigated wetland environments.Full Text
References
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- Comparison of Parametric and Non-Parametric Methods for Chlorophyll Estimation based on High-Resolution UAV Imagery
Abstract Views :253 |
PDF Views:82
Authors
Gaurav Singhal
1,
Babankumar Bansod
2,
Lini Mathew
3,
Jonali Goswami
4,
B. U. Choudhury
5,
P. L. N. Raju
4
Affiliations
1 CSIR-Central Scientific Instruments Organization, Chandigarh 160 030, IN
2 Academy of Scientific and Innovative Research, Ghaziabad 201 002, IN
3 National Institute of Technical Teachers, Training and Research, Chandigarh 160 019, IN
4 North East Space Application Centre, Barapani 793 103, IN
5 ICAR Research Complex for NEH Region, Umiam 793 103, IN
1 CSIR-Central Scientific Instruments Organization, Chandigarh 160 030, IN
2 Academy of Scientific and Innovative Research, Ghaziabad 201 002, IN
3 National Institute of Technical Teachers, Training and Research, Chandigarh 160 019, IN
4 North East Space Application Centre, Barapani 793 103, IN
5 ICAR Research Complex for NEH Region, Umiam 793 103, IN
Source
Current Science, Vol 117, No 11 (2019), Pagination: 1874-1879Abstract
The present study provides a systematic comparison of parametric and non-parametric retrieval methods using high-resolution data provided by the unmanned aerial vehicle (UAV). We used turmeric crop reflectance data to evaluate the vegetation index (VI)-based parametric methods and compared them with linear and nonlinear non-parametric methods to build a rigorous LCC estimation model. The study demonstrates that the best-performing VI was the normalized green red difference index (GNRDI), with R2 = 0.68, RMSE = 0.13 and high processing speed of 0.08 s. With regard to non-parametric methods, almost all methods outperformed their parametric counterparts. Particularly, methods such as random forest (RF) and kernel ridge regression (KRR) showed the best performance characterized by R2 > 0.72 and RMSE ≤ 0.12 mg/g of fresh leaf weight. These nonparametric methods possessed the benefit of total spectral information utilization and enabled robust, non-linear relationship between the predictor and target variables, but computational complexity is a major drawback.Keywords
Chlorophyll, Machine Learning, Unmanned Aerial Vehicle, Vegetation Index.Full Text
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