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Global retrieval of canopy chlorophyll content from Sentinel-3 OLCI TOA data using a two-step upscaling method integrating physical and machine learning models
Canopy chlorophyll content per ground area (CCC, g·m−2) is tightly related to vegetation photosynthesis and is a promising indicator of …
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Characterization of N variations in different organs of winter wheat and mapping NUE using low altitude UAV-based remote sensing
Although unmanned aerial vehicle (UAV) remote sensing is widely used for high-throughput crop monitoring, few attempts have been made …
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Drone multispectral imaging captures the effects of soil mineral nitrogen on canopy structure and nitrogen use efficiency in wheat
Drone remote sensing offers a powerful tool for monitoring vegetation and agricultural systems. However, its effectiveness in assessing …
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Drone RGB Imagery Color and Texture Information Have Varied Importance in Predicting Potato Aboveground Biomass in Different Growth Stages
Accurately monitoring the crop aboveground biomass (AGB) is critical for yield forecasting. Despite the advances in high-resolution RGB …
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Improving potato leaf chlorophyll content prediction using a machine learning model with a hybrid dataset

Haibo Yang, Yuncai Hu, Hang Yin, Qingyu Jin, Fei Li, & Kang Yu (2025). Improving potato leaf chlorophyll content prediction using a machine learning model with a hybrid dataset. International Journal of Remote Sensing: 1–25.

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Improving the performance of plant nitrogen assessment in drip-irrigated potatoes using optimized spectral indices-based machine learning
Timely and accurate monitoring of plant nitrogen concentration (PNC) is vital for optimizing field N management. Hyperspectral indices …
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Improving the transferability of potato nitrogen concentration estimation models based on hybrid feature selection and Gaussian process regression
Feature selection methods are widely used to improve the performance of plant nitrogen concentration (PNC) estimation models. However, …
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Improving winter wheat plant nitrogen concentration prediction by combining proximal hyperspectral sensing and weather information with machine learning
Timely and accurate prediction of nitrogen (N) status in winter wheat is crucial for guiding precision N management. This study aimed …
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NTRI: A novel spectral index for developing a precise nitrogen diagnosis model across pre- and post-anthesis stages of maize plants
Context Accurate and real-time diagnosis of crop nitrogen (N) status is essential for effective precision N management. Integrating the …
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Optimising Root and Grain Yield Through Variety Selection in Winter Wheat Across a European Climate Gradient
Ensuring food security through sustainable practices while reducing greenhouse gas emissions are key challenges in modern agriculture. …
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