Determination of Crop Health Monitoring in MPKV – Rahuri, Using Remote Sensing Approach
Ankita P. Kamble
*
Department of Agricultural Botany, Post Graduate Institute, Mahatma Phule Krushi Vidyapeeth, Rahuri Dist- Ahmednagar, Maharashtra, 413722, India.
A. A. Atre
Centre for Advanced Agricultural Science and Technology for Climate Smart Agriculture and Water Management (CAAST-CSAWM), Rahuri Dist- Ahmednagar, Maharashtra, 413722, India.
Payal A. Mahadule
Centre for Advanced Agricultural Science and Technology for Climate Smart Agriculture and Water Management (CAAST-CSAWM), Rahuri Dist- Ahmednagar, Maharashtra, 413722, India.
C. B. Pande
Centre for Advanced Agricultural Science and Technology for Climate Smart Agriculture and Water Management (CAAST-CSAWM), Rahuri Dist- Ahmednagar, Maharashtra, 413722, India.
N. S. Kute
Pulses Improvement Project, Mahatma Phule Krushi Vidyapeeth, Rahuri Dist- Ahmednagar, Maharashtra, 413722, India.
S. D. Gorantiwar
Centre for Advanced Agricultural Science and Technology for Climate Smart Agriculture and Water Management (CAAST-CSAWM), Rahuri Dist- Ahmednagar, Maharashtra, 413722, India.
*Author to whom correspondence should be addressed.
Abstract
Pests and diseases cause major harm during crop development. Also plant stress affects crop quality and quantity. Recent developments in high resolution remotely sensed data has seen a great potential in mapping cropland areas infected by pests and diseases, as well as potential vulnerable areas over expansive areas. Crop health monitoring in this study was carried out using remote sensing techniques. The present study was carried out in MPKV, Rahuri, Ahmednagar District, Maharashtra. Vegetation indices like Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were used to classify the crops into healthy and dead or unhealthy one. Sentinel-2 image data from October 2019 to January 2020 processed in Arc GIS 10.1 were used for this study. Vegetation is a key component of the ecosystem and plays an important role in stabilizing the global environment. The result showed that the average vegetation cover was decreased in the month of November and healthy vegetation was found more in month of October as compared to December and January. This shows that NDVI and SAVI indices for Sentinel-2 images can be used for crop health monitoring.
Keywords: Crop health, monitoring, NDVI, SAVI, remote sensing, ArcGIS.