Chlorophyll detector development based on snapshot-mosaic multispectral image sensing and field wheat canopy processing

Abstract

To achieve rapid nondestructive detection of chlorophyll content in crops in a field environment, we designed a new lightweight device based on a snapshot-mosaic imaging sensor given the sensitive properties of chlorophyll in the red-edge to near-infrared band (700–900 nm) and the advantages of imaging spectroscopy in environmental noise separation. The hardware part of the device includes a spectral camera based on a snapshot-mosaic sensor, a main control unit, a network module, a storage module, a power supply module, and a display and remote-control device. The software part was written using the Qt library and the C++ programming language. It includes the connection and initialization of the sensor, the control of the exposure time, the display of the image in real time, the acquisition and storage, and the calculation of the reflectance. The sensor was tested and calibrated to evaluate the sensor performance. Experimental results show that the different channels of the sensor have an excellent linear relationship for light intensity changes and can be used to measure reflected radiation from crop leaves in a field environment after calibration. At the same time, field experiments on wheat were conducted. A dark channel filtering method for 25 channels of the developed device was proposed to effectively eliminate the scattered light interference from the crop canopy in the spectral images. The background interference was effectively eliminated by background segmentation. Partial least squares regression (PLSR) method was used for modeling after preprocessing and band screening of the spectral data. The model achieved high accuracy with RC2 of 0.87, RV2 of 0.79, and RMSE of 3.94 mg/L. As a result, the system combined the proposed model with the developed device can effectively eliminate the interference of soil background and scattered light caused by the complex structure of crop canopy, and improve the accuracy of diagnosis. So that the system has potential in the crop growth variation analysis.