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

• DOI: 10.3390/agronomy14010124
• Publication Date: 2024-01-03T10:50:38Z
• Authors: Lei Sun; Chongchong Yang; Jun Wang; Xiwen Cui; Xuesong Suo; Xiaofei Fan; Pengtao Ji; Liang Gao; Yuechen Zhang
• Keywords: 0106 biological sciences; S; detection method; automatic modelling; Agriculture; machine vision; maizes leaves; 01 natural sciences; nitrogen content

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 potential impact optimal yield ultimately, profit margins associated its cultivation. In this study, an automatic modeling prediction method for content in leaves was proposed based on machine vision convolutional neural network. We developed program designed streamline image preprocessing workflow. can process multiple images batches, automatically carrying out necessary steps. Additionally, integrates automated training system that correlates values. The primary objective enhance models by leveraging larger dataset samples. Secondly, fully connected layer network reconstructed transform optimization goal from classification 0–1 tags into regression prediction, so model output numerical values content. Furthermore, gained many samples, samples were collected three key additional fertilizing stages throughout growth period (i.e., jointing stage, bell mouth tasseling stage). addition, compared spectral under full-wave band characteristic wavelengths. It verified our CNN (Convolutional Neural Network)-based offers high rate only consistently better—by approximately 5% 45%—than approaches but also features benefits easy operation low cost. technology significantly contribute implementation more precise fertilization practices production, leading increased profitability.