Research On Chlorophyll In Lycium Barbarum Leaves Based On Hyperspectral Images

ModeRn technology plays a vital role in guiding agricultural production.The realization of agricultural intelligent water-saving irrigation,precise fertilization,and monitoring of plant growth status through scientific and effective technical means have become the main trends in the future development of agriculture and one of the hotspots of modern agricultural research.In recent years,with the deepening of scientific and technological cooperation between the east and the west,the basic research on Ningxia wolfberry has also been strengthened.In 2020,in the face of the high-quality development goal of the modern wolfberry industry proposed by Ningxia,efficient and accurate monitoring of the growth of wolfberry has become particularly important.This study realized the prediction of chlorophyll content in wolfberry leaves based on hyperspectral images.Compared with traditional multispectral images,hyperspectral images provide high-dimensional spectral information to extract rich band reflectance.A prediction model of leaf chlorophyll content was established by using artificial neural network through reflectance and the first derivative of reflectance.Using the information of chlorophyll content to judge the ability of crop photosynthesis provides a favorable technical guarantee for realizing water-saving intelligent irrigation,precise fertilization,and accelerating the high-quality development of the modern wolfberry industry.Related studies are as follows:(1)Perform post-difference POS data processing,route segmentation,hyperspectral image segmentation,image georeferencing,hypercube production,and reflectivity conversion on the collected hyperspectral images;The reflectance curve is smoothed and denoised to obtain the original reflectance,and then the original reflectance is subjected to spectral differentiation processing,and the result is the first derivative of the reflectance.(2)The wavelengths of 400-1000 nm are divided into 150 bands,and the first derivative of reflectance and reflectance obtained above in this band range corresponds to the chlorophyll content of 102 sample leaves collected by SPAD-502 chlorophyll meter.The samples are divided into training set and test set proportionally,and the training samples are based on BP neural network and Elman neural network,and the prediction results are evaluated by the coefficient of determination R2,the root mean square error RMSE,and the mean absolute error MAE.The BP neural network obtains the optimal prediction model when the input variable is the first derivative of reflectivity and the number of test sets is 12.At this time,RMSE is 4.1527;MAE is 2.8582;R2 is 0.52610;the optimal model of Elman neural network is the first-order reflectance model.When the number of derivative test sets is 12,the network RMSE is 3.4814;MAE is 3.0245;R2 is 0.47506.(3)In view of the low prediction accuracy of the model network,the local optimal problems in the training process,and the poor stability of the network prediction results due to the random extraction of weights and thresholds,the optimization of BP neural network based on genetic algorithm and the optimization of particle swarm optimization are proposed respectively.Elman neural network.After optimization,the results of the optimal prediction models for the two types of networks are as follows:when the input variable of the BP network optimized by the genetic algorithm is the first derivative of the reflectivity and the number of test sets is 11,the RMSE is 2.0248;the MAE is 1.5945;the R2 is 0.87030;the particle swarm optimization Elman When the network input variable is the first derivative of the reflectance,the test set is also 11,the RMSE is 0.2554;the MAE is 0.20942;and the R2 is 0.98967.(4)By comparing the determination coefficients of each model,it is found that the Elman neural network optimized by particle swarm optimization is the optimal prediction model of leaf chlorophyll.Using the interface design function of MATLAB2018b,programming and designing the computer-side prediction operation interface,realizing the use of this model,and using the first derivative of the original reflectance as the input variable,the content of chlorophyll in wolfbery leaves can be predicted.