Development of crop chlorophyll detector based on a type of interference filter optical sensor

• Published in: Computers and electronics in agriculture Vol. 187; p. 106260
• Main Authors: Song, Di, Qiao, Lang, Gao, Dehua, Li, Song, Li, Minzan, Sun, Hong, Ma, Junyong
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.08.2021; Elsevier BV
• Subjects: Calibration; Chlorophyll; Chlorophyll content; Crop growth; Crops; Data processing; Data storage; Infrared spectra; Interference; Interference filtering; Luminous intensity; Microcontrollers; Modules; Multi-band sensor; Near infrared radiation; Non-destructive detection; Optical measuring instruments; Performance evaluation; Programming languages; Regression analysis; Sensors; Spectral analysis; Non-destructive detection; Chlorophyll content; Interference filtering; Multi-band sensor; Spectral analysis
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2021.106260

•Carry out hardware and software development.•Propose the range of wavelength selection and choose a new type of sensor.•After testing and calibration, the accuracy of detector has been increased.•Realize the non-destructive and rapid detection of the chlorophyll content. To achieve a non-destructive detection of chlorophyll content in field crops based on the reflectance characteristics of chlorophyll in the visible and near-infrared spectrum (400 nm–1000 nm), a crop chlorophyll detector based on an interference filter optical sensor was designed. The hardware part of this detector mainly comprises a microcontroller unit, a sensor module, an input/output module, and a power module. The software is written in Python language and includes main functions, acquisition sub-functions, data processing sub-functions, and data storage sub-functions. Calibration and test experiments were carried out to evaluate the performance of the sensor. Results show that the sensor has a good responsivity of light intensity changes, so as to measure the reflected radiation from crops with the absorption of chlorophyll content. Field verification experiments of corn crops were also carried out, and chlorophyll content detecting models were built by using four combinations of characteristic wavelengths, including 3 peak bands, 9 bands selected via the stepwise regression analysis method, 8 bands selected via the Monte Carlo uninformed variable elimination method, and all 18 bands. Among them, the stepwise regression method obtained the best modeling results. The model showed better performance after calibration than before the calibration with RC2 of 0.72, RV2 of 0.61, RMSEc of 2.35 mg/L, and RMSEv of 2.43 mg/L. The crop chlorophyll detector based on the interference filter optical sensor was used for filed estimation of chlorophyll content which showed a potential for the analysis of crop growth differences.