Sci Rep 11, 12693 (2021)
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).
Md Abir Hossen, Prasoon K Diwaka, Shankarachary Ragi
2021-07-06
