Total Nitrogen Estimation in Agricultural Soils via Aerial Multispectral Imaging and LIBS
This work addresses the problem of low-throughput soil health measurement for farmers, enabling near real-time nitrogen estimation to improve fertilizer decisions and crop yield, though it is incremental as it applies existing machine learning methods to a new agricultural data context.
The paper tackles the challenge of measuring soil total nitrogen by developing an AI-driven UAV-based multispectral sensing solution, achieving accurate prediction using machine learning models trained on multispectral and environmental data with ground truth from LIBS.
Measuring soil health indicators is an important and challenging task that affects farmers' decisions on timing, placement, and quantity of fertilizers applied in the farms. Most existing methods to measure soil health indicators (SHIs) are in-lab wet chemistry or spectroscopy-based methods, which require significant human input and effort, time-consuming, costly, and are low-throughput in nature. To address this challenge, we develop an artificial intelligence (AI)-driven near real-time unmanned aerial vehicle (UAV)-based multispectral sensing (UMS) solution to estimate total nitrogen (TN) of the soil, an important macro-nutrient or SHI that directly affects the crop health. Accurate prediction of soil TN can significantly increase crop yield through informed decision making on the timing of seed planting, and fertilizer quantity and timing. We train two machine learning models including multi-layer perceptron and support vector machine to predict the soil nitrogen using a suite of data classes including multispectral characteristics of the soil and crops in red, near-infrared, and green spectral bands, computed vegetation indices, and environmental variables including air temperature and relative humidity. To generate the ground-truth data or the training data for the machine learning models, we measure the total nitrogen of the soil samples (collected from a farm) using laser-induced breakdown spectroscopy (LIBS).