Research on High-Accuracy, Lightweight, Superfast Model for Nitrogen Diagnosis and Plant Growth in Lettuce (Lactuca sativa L.)

Abstract

Nitrogen is a crucial environmental factor influencing lettuce growth, development, and quality formation. This study aimed to determine the relationship between plant growth, nutritional quality formation, and different nitrogen levels of lettuce. A machine learning approach was also applied to data collected from RGB and hyperspectral imaging systems. Traditional methods for nitrogen diagnosis in lettuce, such as laboratory-based analysis of plant samples, are labor-intensive, time-consuming, and lack real-time monitoring capabilities. In contrast, the deep learning models used in this research can make full use of the original data from imaging systems. Nondestructive techniques have the ability to handle complex relationships in the data, enabling more accurate and efficient nitrogen diagnosis. Collected spectral features were combined with chemometrics, and a lettuce nitrogen regression diagnostic model was trained. Furthermore, lettuce crop growth was assessed using a model development of environmental and plant physiological parameters. Additionally, nitrogen fertilization was precisely assessed using developed models. Lettuce cultivation experiments under different nitrogen levels showed the best physiological and biochemical indicators performance when the nitrogen concentration reached 18.75 mmol·L−1. Using machine learning with hyperspectral reflectance in nitrogen diagnostics, random forest showed excellent performance with the highest R2, MSE, and MAE of 0.7012, 8.940, and 2.1859, respectively. ShuffleNet-v2-1.0 obtained a high R2 of 0.9592, MSE of 132.9974, and MAE of 8.1430 regarding transfer learning and hyperspectral images. Applying the transfer learning technique in RGB images exhibited EfficientNet-v2-s, the best model for precise determination of nitrogen diagnostics, with R2 of 0.9859, MSE of 24.0755, and MAE of 2.3433. Current research comprehensively provides both a theoretical basis and practical solutions for precision nitrogen fertilization in lettuce cultivation. Its implications hold significance for the intelligent management of horticultural crop production.

Plant phenotyping relevance

RGB・ハイパースペクトル画像と機械学習によるレタスの窒素状態診断モデルを開発・評価しており、植物状態の取得・推定手法が研究の中心である。

Code and data availability

The supplied blocks describe lettuce RGB/hyperspectral image collection and model training (U2Net segmentation, random forest, transfer learning CNNs), but contain no public dataset deposit, no author code/workflow URL, and no trained model release. Supplementary figures are referenced but not described as containing a

No evidence-backed public reproduction asset is currently recorded.