Quantification of groundnut leaf defects using image processing algorithms
This work addresses crop monitoring for farmers by providing an automated method to identify and quantify leaf defects, enabling targeted pesticide application to reduce yield loss, though it is incremental as it applies existing image processing techniques to a specific agricultural context.
The study tackled the problem of quantifying groundnut leaf defects by using UAV-based image processing to estimate affected leaf area, finding that 14-28% of leaves were damaged across four regions in Andhra Pradesh.
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.