Crop type discrimination through low cost proximal RGB imaging and multivariate analysis

The current study is an attempt to use low cost red green blue (RGB) image–based vegetation indices (VIs), obtained from simple RGB camera, in separating six different field crops. To achieve this, sixteen VIs were calculated and used as inputs in different multivariate analysis for separating wheat (Triticum spp), mustard (Brassica spp), cabbage (Brassica oleracea), pigeon pea (Cajanus cajan), brinjal (Solanum app) and chickpea (Cicer arietinum). Based on the classification and regression tree (CART) analysis, the study identified Green Red Ratio Index (GRRI), Color Intensity Index (INT), Color Index Of Vegetation (CIVE) and Woebbecke Index (WI) were statistically significant (p < 0.05) in discriminating six different crops. The results obtained from CART analysis were subsequently compared with discriminant analysis, which showed an accuracy of 96.3% of classifying different crops. Hence, out of 16 indices, the study meaningfully identified four most sensitive VIs that can be used to classify different field crops. The information achieved in this study can help in commercial and scientific decision-making, planning in agribusinesses, and can be an important tool for conducting crop survey at regional scale.