Fractional crop cover estimation via drone imagery and machine learning with color models

Abstract

Drones have become increasingly popular in precision agriculture due to their ability to collect valuable data quickly and efficiently. One of the major aspects of precision agriculture is to estimate fraction crop cover at an early stage. This paper develops an approach using fine-tuned Machine Learning (ML) YOLO models to extract crop fields only from drone imagery and mask all other objects. A combination of Color Space Models (CSM) is used to extract fraction crop cover at an early stage. The approach was developed for extracting information with less processing complexity as drones/UAVs have limited processing and power capabilities. The primary objective of this study is to identify low-density crop areas and barren land within crop fields during the early stages of crop growth using CSM. Otsu and Max Entropy thresholding techniques are analysed to obtain mask information of targeted area. Morphological open and close operations are used to get desired size patches of sparse or no crop location. The study suggests Otsu thresholding as an adaptive thresholding method as its results are adequate compared to ground truth. Different filter size results are also compared as filter size determines the minimum patch size identified on the ground. The finetuned ML model extracts the object of interest. The color space model works well when applied to that single object.