Study on Deep Learning Model for Online Estimation of Chlorophyll Content Based on Near Ground Multispectral Feature Bands

Abstract

Chlorophyll content of plant leaves is an essential indicator of crop growth status. This study is focused on the nondestructive estimation of the chlorophyll content of maize using near ground multispectral data. We propose a one-dimensional convolutional neural network-gated recurrent unit (1-D-CNN-GRU). That is, it combines a 1D-CNN with strong feature expression capacity and strong memory capacity with the gated recurrent unit (GRU) neural network to estimate of the chlorophyll content of maize directly from multispectral images. Furthermore, the iteratively retaining informative variables-successive projections algorithm (IRIV-SPA) is first used to select the feature wavebands from the 11 available wavebands of two datasets in the experiment. The experimental results show that the selected feature wavebands have more accuracy than the raw wavebands when using the same model; based on these feature wavebands, the 1D-CNN-GRU model has smaller errors than the other conventional models such as support vector regression (SVR) and random forest (RF), with an MRE of 0.069, RMSE of 3.473 on Datasets I, and an MRE of 0.108, RMSE of 7.568 on Datasets II. Meanwhile, the real-time performance is also validated in the experiment. These investigations can provide a valuable guidance for online monitoring chlorophyll content of crops such as maize etc., based on near ground multispectral band data processing for developing smart agricultural system.