APiCroDD: Automated Pipeline for Crop Disease Detection

Abstract

This research paper proposes APiCroDD: automated pipeline for crop disease detection, an automated framework for early detection of plant diseases using multispectral imagery from drones. Current frameworks for disease detection are labor and time-consuming. They do not leverage the richness of multispectral imagery for feature extraction and perform vanilla manipulation of agriculture indices. Our framework comprises two stages: data acquisition and disease identification. We find that the use of multispectral imagery in the proposed framework provides several advantages over traditional RGB imagery, including better spectral resolution and increased sensitivity to subtle changes in plant health. The multispectral data enables the identification of specific spectral bands associated with diseased regions of the plant, improving the accuracy of disease detection. The proposed framework utilizes a combination of CNNs and segmentation techniques to identify the plant and its disease. Experimental results demonstrate that the proposed framework using EfficientNet is highly effective in identifying a range of plant diseases achieving state-of-the-art performance on manually collected dataset and validated on the PlantVillage dataset.