Unmanned aerial vehicle(UAV)-based multispectral imaging is one of the most widely used technologies for rapid crop monitoring,essential for crop-growth management.However,the technology's complex optical structur...Unmanned aerial vehicle(UAV)-based multispectral imaging is one of the most widely used technologies for rapid crop monitoring,essential for crop-growth management.However,the technology's complex optical structure and difficulty in interpreting real-time crop-growth information seriously restrict its application.This paper presents a newly designed UAV-based snapshot multispectral imaging crop-growth sensor(SMICGS)aimed at simplifying the optical structure and realizing the online interpretation of crop spectral information.Mosaic filters based on the special spectral characteristics of crops were designed to achieve multiband co-optical im-aging.A spectral crosstalk correction method based on the pixel response characteristics of SMICGS was pro-posed,and a processing system based on the coupling of sensor information and crop-growth monitoring models was developed to realize real-time online processing of crop spectral information.Field experiments showed that the vegetation indices obtained by SMICGS combined with the machine learning algorithm random forest(RF)achieved better results in predicting leaf area index(LAI)and above-ground biomass(AGB)for wheat and rice.For wheat,the R^(2) and root mean square error(RMSE)values for the LAI and AGB prediction models were 0.81 and 0.85,and 0.682 and 1.127 t/ha,respectively.For rice,the R^(2) and RMSE values for the LAI and AGB pre-diction models were 0.89 and 0.93,and 0.818 and 0.866 t/ha,respectively.Overall,SMICGS provides a reliable foundational tool for real-time,non-destructive monitoring of field crop growth information,offering significant potential for the precise management of agricultural production.展开更多
Granulated coconut sugar has been well-known as a sweetener which is more nutritious and has lower glycemic index than cane sugar.Adding cane sugar to coconut sap during heating may result in coconut sugar with an und...Granulated coconut sugar has been well-known as a sweetener which is more nutritious and has lower glycemic index than cane sugar.Adding cane sugar to coconut sap during heating may result in coconut sugar with an undesirable export quality.The purpose of this study was to develop a novel approach by designing a low-cost portable spectrometer capable of detecting the presence of cane sugar in granulated coconut sugar using machine learning.The AS7265x multispectral sensor chipset is the main component of the proposed LED-based spectrometer.This chipset uses two integrated LEDs as the light source and has 18 channels output ranging from the visible to near-infrared spectrum as the predictor variables to identify the adulteration in granulated coconut sugar.A variety of machine learning techniques were used to determine the purity of granulated coconut sugar as well as the quantity of cane sugar added.Backpropagation neural networks outperformed various machine learning methods,including the support vector machine,k-nearest neighbor,and naïve Bayes methods,in determining the purity of granulated coconut sugar.The developed portable LED-based spectrometer by means of backpropagation neural networks as the classifier can successfully detect adulteration in granulated coconut sugar with very high accuracy level.展开更多
基金This work was supported by the National Key Research and Development Program of China(2021YFD2000101)Jiangsu Province Science Foundation for Youths(BK20241544).
文摘Unmanned aerial vehicle(UAV)-based multispectral imaging is one of the most widely used technologies for rapid crop monitoring,essential for crop-growth management.However,the technology's complex optical structure and difficulty in interpreting real-time crop-growth information seriously restrict its application.This paper presents a newly designed UAV-based snapshot multispectral imaging crop-growth sensor(SMICGS)aimed at simplifying the optical structure and realizing the online interpretation of crop spectral information.Mosaic filters based on the special spectral characteristics of crops were designed to achieve multiband co-optical im-aging.A spectral crosstalk correction method based on the pixel response characteristics of SMICGS was pro-posed,and a processing system based on the coupling of sensor information and crop-growth monitoring models was developed to realize real-time online processing of crop spectral information.Field experiments showed that the vegetation indices obtained by SMICGS combined with the machine learning algorithm random forest(RF)achieved better results in predicting leaf area index(LAI)and above-ground biomass(AGB)for wheat and rice.For wheat,the R^(2) and root mean square error(RMSE)values for the LAI and AGB prediction models were 0.81 and 0.85,and 0.682 and 1.127 t/ha,respectively.For rice,the R^(2) and RMSE values for the LAI and AGB pre-diction models were 0.89 and 0.93,and 0.818 and 0.866 t/ha,respectively.Overall,SMICGS provides a reliable foundational tool for real-time,non-destructive monitoring of field crop growth information,offering significant potential for the precise management of agricultural production.
文摘Granulated coconut sugar has been well-known as a sweetener which is more nutritious and has lower glycemic index than cane sugar.Adding cane sugar to coconut sap during heating may result in coconut sugar with an undesirable export quality.The purpose of this study was to develop a novel approach by designing a low-cost portable spectrometer capable of detecting the presence of cane sugar in granulated coconut sugar using machine learning.The AS7265x multispectral sensor chipset is the main component of the proposed LED-based spectrometer.This chipset uses two integrated LEDs as the light source and has 18 channels output ranging from the visible to near-infrared spectrum as the predictor variables to identify the adulteration in granulated coconut sugar.A variety of machine learning techniques were used to determine the purity of granulated coconut sugar as well as the quantity of cane sugar added.Backpropagation neural networks outperformed various machine learning methods,including the support vector machine,k-nearest neighbor,and naïve Bayes methods,in determining the purity of granulated coconut sugar.The developed portable LED-based spectrometer by means of backpropagation neural networks as the classifier can successfully detect adulteration in granulated coconut sugar with very high accuracy level.
