Quantification of groundnut leaf defects using image processing algorithms

TL;DR

This paper presents an image processing approach using UAV data to quantify groundnut leaf defects across multiple regions, enabling targeted pesticide application to improve crop yield.

Contribution

It introduces a UAV-based image segmentation method combining color space transformation and thresholding for quantifying leaf defects.

Findings

14-28% of leaf area affected across regions

Method accurately estimates defected areas

Targeted pesticide application recommended

Abstract

Identification, classification, and quantification of crop defects are of paramount of interest to the farmers for preventive measures and decrease the yield loss through necessary remedial actions. Due to the vast agricultural field, manual inspection of crops is tedious and time-consuming. UAV based data collection, observation, identification, and quantification of defected leaves area are considered to be an effective solution. The present work attempts to estimate the percentage of affected groundnut leaves area across four regions of Andharapradesh using image processing techniques. The proposed method involves colour space transformation combined with thresholding technique to perform the segmentation. The calibration measures are performed during acquisition with respect to UAV capturing distance, angle and other relevant camera parameters. Finally, our method can estimate the consolidated leaves and defected area. The image analysis results across these four regions reveal that around 14 - 28% of leaves area is affected across the groundnut field and thereby yield will be diminished correspondingly. Hence, it is recommended to spray the pesticides on the affected regions alone across the field to improve the plant growth and thereby yield will be increased.