CIG based Stress Identification Method for Maize Crop using UAV based Remote Sensing

The health and yield of crops depend on the use of water, nutrients, and fertilizers. Due to climatic changes and reduction in rainfall, farmers are relying on groundwater for irrigation, which should be used optimally. The use of water and other agronomic inputs can be optimized by monitoring the health of crops and soil. Usually, it is done by manual observation, which is labor-intensive and time-consuming. In this paper, we propose Chlorophyll Index Green (CIG) vegetative index-based method for monitoring the crop health using near-infrared, green, and red band images acquired using a multispectral camera mounted on Unmanned Ariel Vehicle (UAV). The proposed method clearly classifies the water-stressed area of the field and helps in optimizing the irrigation process and monitoring the crop-health.