Data-Driven Prediction of Grape Leaf Chlorophyll Content Using Hyperspectral Imaging and Convolutional Neural Networks

Grapes, highly nutritious and flavorful fruits, require adequate chlorophyll to ensure normal growth and development. Consequently, the rapid, accurate, and efficient detection of chlorophyll content is essential. This study develops a data-driven integrated framework that combines hyperspectral imaging (HSI) and convolutional neural networks (CNNs) to predict the chlorophyll content in grape leaves, employing hyperspectral images and chlorophyll a + b content data. Initially, the VGG16-U-Net model was employed to segment the hyperspectral images of grape leaves for leaf area extraction. Subsequently, the study discussed 15 different spectral preprocessing methods, selecting fast Fourier transform (FFT) as the optimal approach. Twelve one-dimensional CNN models were subsequently developed. Experimental results revealed that the VGG16-U-Net-FFT-CNN1-1 framework developed in this study exhibited outstanding performance, achieving an R2 of 0.925 and an RMSE of 2.172, surpassing those of traditional regression models. The t-test and F-test results further confirm the statistical robustness of the VGG16-U-Net-FFT-CNN1-1 framework. This provides a basis for estimating chlorophyll content in grape leaves using HSI technology.