Research on Polarized Multi-Spectral System and Fusion Algorithm for Remote Sensing of Vegetation Status at Night

Abstract

The monitoring of vegetation via remote sensing has been widely applied in various fields, such as crop diseases and pests, forest coverage and vegetation growth status, but such monitoring activities were mainly carried out in the daytime, resulting in limitations in sensing the status of vegetation at night. In this article, with the aim of monitoring the health status of outdoor plants at night by remote sensing, a polarized multispectral low-illumination-level imaging system (PMSIS) was established, and a fusion algorithm was proposed to detect vegetation by sensing the spectrum and polarization characteristics of the diffuse and specular reflection of vegetation. The normalized vegetation index (NDVI), degree of linear polarization (DoLP) and angle of polarization (AOP) are all calculated in the fusion algorithm to better detect the health status of plants in the night environment. Based on NDVI, DoLP and AOP fusion images (NDAI), a new index of night plant state detection (NPSDI) was proposed. A correlation analysis was made for the chlorophyll content (SPAD), nitrogen content (NC), NDVI and NPSDI to understand their capabilities to detect plants under stress. The scatter plot of NPSDI shows a good distinction between vegetation with different health levels, which can be seen from the high specificity and sensitivity values. It can be seen that NPSDI has a good correlation with NDVI (coefficient of determination R2 = 0.968), PSAD (R2 = 0.882) and NC (R2 = 0.916), which highlights the potential of NPSDI in the identification of plant health status. The results clearly show that the proposed fusion algorithm can enhance the contrast effect and the generated fusion image will carry richer vegetation information, thereby monitoring the health status of plants at night more effectively. This algorithm has a great potential in using remote sensing platform to monitor the health of vegetation and crops.

A New Polarization-Based Vegetation Index to Improve the Accuracy of Vegetation Health Detection by Eliminating Specular Reflection of Vegetation

“…Our previous work studied the application of detecting the vegetation condition through remote sensing during the nighttime outdoors [49]. Based on PMSIS, a new method of night plant status detection was proposed to detect vegetation to better monitoring of plant health in the nighttime environment.…”

“…Li et al [49] came up with the night plant status detection index (NPSDI), a new index for characterizing the status of plants at night, to detect the physiological condition of vegetation at night. At present, most of the remote sensing research is carried out in the light environment, and there are few related researches on the monitoring of vegetation health status at night.…”

SCANet: Implementation of Selective Context Adaptation Network in Smart Farming Applications

“…By automating packaging or sorting part of the process, production cost can be kept low. Along the ripeness of the crop, specific conditions of the produce can also be monitored by using object detection algorithm [ 13 , 14 , 15 ]. Particularly, the health of the vegetation or its yield is in general interest, whether the degradation is caused by pest, parasite, or environmental factors.…”

High-Efficiency Multispectral-Polarization Imaging System Using Polarization Camera Array With Notch Filters

“…This technique leads to poor real-time performance and is unsuitable for dynamic imaging. For instance, certain multispectral and polarization imaging systems [12], [13], [14] utilize rotating polarizers, quarter-wave plates, and custom illumination units to precisely capture the polarization state and spectral images of subjects or specimens. The division-of-amplitude method [15] uses light splitters and polarizers of different angles in the optical path to obtain polarization information.…”