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

Chlorophyll content in plant leaves is an essential indicator of the crop growth status. This study focuses on 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 a gated recurrent unit (GRU) neural network to estimate 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 the two datasets in the experiment. The experimental results show that the selected feature wavebands are more accurate 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 mean relative error (MRE) of 0.069, root mean square error (RMSE) of 3.473 on Datasets I, and an MRE of 0.108, RMSE of 7.568 on Datasets II. The real-time performance is also validated in the experiment. These investigations can provide valuable guidelines for online monitoring of chlorophyll content in maize based on near earth multispectral band data, and are also important references for the development of intelligent agricultural monitoring systems for general crops, which were tested on maize only and provided reliable results in this study.