Potato Crop Health Assessment Using Multispectral Image Analysis

Abstract

In order to categorize healthy and stressed potato crops obtained from UAV-based multispectral photos, this study will use machine learning. It also aims to determine which spectral band offers the best separation for classification. These machine learning techniques are compared to classify healthy and stressed crops: Random Forest Classifier (RF), K-Nearest Neighbors Classifier (KNN), and Gradient Boosting Classifier (GBC), as well as Support Vector Classifier (SVC) and Linear Support Vector Classifier (LSVC). The Normalized Difference Vegetation Index (NDVI) and the Red Edge Normalized Difference Vegetation Index (NDRE), RED, GREEN, Near-Infrared (NIR), REDEDGE bands, are used by machine learning techniques to categorize data. The suggested approach consists of three main parts: (1) extraction of vegetation indicators; (2) Utilising thresholding technique to remove the background containing weeds and soil; and (3) classification and model training using machine learning classifier. As a consequence of its strong performance and good accuracy (0.777% accuracy and AUC score of 0.92), the paper’s findings demonstrate that RF is the best appropriate classifier for healthy and unhealthy plants. The NIR band is the most accurate in spotting unhealthy plants, with a 0.169 feature significance score. This band is connected to the two vegetative indices, NDVI and NDR, which had the best spectral characteristics in the classification model, with feature significance scores of 0.189 and 0.181, respectively.