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Nitrogen (N) transformation in soils is crucial in determining N availability for plant growth. Row crop producers have widely adopted cover crops across the U.S. However, there is limited knowledge about N transformations in commercial fields with and without cover crops. Furthermore, there is lack of understanding about the spatial variability of potential N mineralization in row crops and how it varies within a field and between cropping systems. An in-situ mineralization study was conducted in two Alabama row crop farms to evaluate the variability of potential N mineralization across locations and within a farm. The results revealed the variability in N mineralization within farms at both the locations. It was reported that a farm with cover crop and residue retention history had a mineralization rate of 1.18 to 3.89 lb/acre/day. In contrast, another farm with no cover crop had a mineralization potential of 0.93 to 1.17 lb/acre/day. These findings underscore the importance of cover crops and residue retention for enhancing N mineralization potential.
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Total nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS
Abstract Measuring soil health indicators (SHIs), particularly soil total nitrogen (TN), 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 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 solution (UMS) to estimate soil TN in an agricultural farm. TN is 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. The ground-truth data required to train the AI approaches is generated via laser-induced breakdown spectroscopy (LIBS), which can be readily used to characterize soil samples, providing rapid chemical analysis of the samples and their constituents (e.g., nitrogen, potassium, phosphorus, calcium). Although LIBS was previously applied for soil nutrient detection, there is no existing study on the integration of LIBS with UAV multispectral imaging and AI. We train two machine learning (ML) models including multi-layer perceptron regression and support vector regression to predict the soil nitrogen using a suite of data classes including multispectral characteristics of the soil and crops in red (R), near-infrared, and green (G) spectral bands, computed vegetation indices (NDVI), and environmental variables including air temperature and relative humidity (RH). To generate the ground-truth data or the training data for the machine learning models, we determine the N spectrum of the soil samples (collected from a farm) using LIBS and develop a calibration model using the correlation between actual TN of the soil samples and the maximum intensity of N spectrum. In addition, we extract the features from the multispectral images captured while the UAV follows an autonomous flight plan, at different growth stages of the crops. The ML model’s performance is tested on a fixed configuration space for the hyper-parameters using various hyper-parameter optimization techniques at three different wavelengths of the N spectrum.
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- Award ID(s):
- 1849206
- PAR ID:
- 10251764
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-021-90624-6
