Digital image processing methods for estimating leaf area of cucumber plants

Abstract

Increasingly <span>emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and non-destructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring </span>systems.

Detection of the patient with COVID-19 relying on ML technology and FAST algorithms to extract the features

“…In terms of accuracy, the maximum level is 1.0, and the lowest is 0.0. ERR may also be employed to measure it, as shown by (1).…”

“…The World Health Organization has confirmed the existence of COVID-19, a new coronavirus illness discovered in Wuhan, China, in December 2019 [1]. With no effective treatment, this virus spread quickly within and beyond China, causing severe acute respiratory syndrome (SARS) in people who got afflicted.…”