Automatic Modeling Prediction Method of Nitrogen Content in Maize Leaves Based on Machine Vision and CNN

Existing maize production is grappling with the hurdles of not applying nitrogen fertilizer accurately due to subpar detection accuracy and responsiveness. This situation presents a significant challenge, as it has the potential to impact the optimal yield of maize and ultimately, the profit margins associated with its cultivation. In this study, an automatic modeling prediction method for nitrogen content in maize leaves was proposed based on machine vision and convolutional neural network. We developed a program designed to streamline the image preprocessing workflow. This program can process multiple images in batches, automatically carrying out the necessary preprocessing steps. Additionally, it integrates an automated training and modeling system that correlates the images with nitrogen content values. The primary objective of this program is to enhance the accuracy of the models by leveraging a larger dataset of image samples. Secondly, the fully connected layer of the convolutional neural network was reconstructed to transform the optimization goal from classification based on 0-1 tags into regression prediction, so that the model can output numerical values of nitrogen content. Furthermore, the prediction model of nitrogen content in maize leaves was gained by training many samples, and samples were collected in three key additional fertilizing stages throughout the growth period of maize (i.e., jointing stage, bell mouth stage, and tasseling stage). In addition, the proposed method was compared with the spectral detection method under full-wave band and characteristic wavelengths. It was verified that our machine vision and CNN (Convolutional Neural Network)-based method offers a high prediction accuracy rate that is not only consistently better-by approximately 5% to 45%-than spectral detection approaches but also features the benefits of easy operation and low cost. This technology can significantly contribute to the implementation of more precise fertilization practices in maize production, leading to potential yield optimization and increased profitability.