SC-ResNeXt: A Regression Prediction Model for Nitrogen Content in Sugarcane Leaves

Agronomy, Journal Year: 2025, Volume and Issue: 15(1), P. 175 - 175

Published: Jan. 13, 2025

In agricultural production, the nitrogen content of sugarcane is assessed with precision and economy, which crucial for balancing fertilizer application, reducing resource waste, minimizing environmental pollution. As an important economic crop, productivity significantly influenced by various factors, especially supply. Traditional methods based on manually extracted image features are not only costly but also limited in accuracy generalization ability. To address these issues, a novel regression prediction model estimating sugarcane, named SC-ResNeXt (Enhanced Self-Attention, Spatial Attention, Channel Attention ResNeXt), has been proposed this study. The Self-Attention (SA) mechanism Convolutional Block Module (CBAM) have incorporated into ResNeXt101 to enhance model’s focus key its information extraction capability. It was demonstrated that achieved test R2 value 93.49% predicting leaves. After introducing SA CBAM attention mechanisms, improved 4.02%. Compared four classical deep learning algorithms, exhibited superior performance. This study utilized images captured smartphones combined automatic feature technologies, achieving precise economical predictions compared traditional laboratory chemical analysis methods. approach offers affordable technical solution small farmers optimize management plants, potentially leading yield improvements. Additionally, it supports development more intelligent farming practices providing predictions.

Language: Английский

Integrative Metabolome Profiling and Docking Study of Sugar-Related Metabolites in Lytic Vacuoles of Mature Sugarcane Stem

Tropical Plant Biology, Journal Year: 2025, Volume and Issue: 18(1)

Published: March 27, 2025

Language: Английский