Coffee Flower Identification Using Binarization Algorithm Based on Convolutional Neural Network for Digital Images.

ABSTRACT: Crop-type identification is one of the most significant applications of agricultural remote sensing, and it is important for yield estimation prediction and field management. At present, crop identification using datasets from unmanned aerial vehicle (UAV) and satellite platforms have achieved state-of-the-art performances. However, accurate monitoring of small plants, such as the coffee flower, cannot be achieved using datasets from these platforms. With the development of time-lapse image acquisition technology based on ground-based remote sensing, a large number of small-scale plantation datasets with high spatial-temporal resolution are being generated, which can provide great opportunities for small target monitoring of a specific region. The main contribution of this paper is to comb

SUBMITTER: Wei P 

PROVIDER: S-EPMC7706348 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature