SpectraNet: A Neural Network for Soybean Contents Prediction

Henry Kobs, Henry Kobs, Henrique Krever, Henrique Krever, Denilson Ebling, Denilson Ebling, Celio Trois

2024

Abstract

Soybeans are integral to global agriculture and food production, playing a vital role in human and animal nutrition. Accurate assessment of moisture, oil, and protein contents in soybeans is crucial for various applications, including human nutrition, animal feed, and food manufacturing. This paper introduces SpectraNet, a Neural Network architecture designed for predicting soybean contents using Near-infrared Spectroscopy (NIRS) data. NIRS technology provides a cost-effective and non-destructive means of analyzing agricultural samples. Spec-traNet leverages a 1D convolutional Neural Network and multiple prediction heads, demonstrating its efficacy in handling non-linearities present in spectral data. The architecture’s flexibility and adaptability contribute to accurate predictions, automatic feature extraction, and suitability for varying conditions. Comparative analysis with traditional Partial Least Squares Regression (PLSR) models reveals the superior performance of SpectraNet in predicting protein, moisture, and oil contents in soybeans. The presented methodology involves comprehensive data collection, laboratory analysis, and model training, showcasing the potential of SpectraNet for real-world applications in agriculture. The results highlight the efficiency and precision of SpectraNet, offering a valuable tool for advancing agricultural practices and ensuring soybean quality.

Paper Citation

in Harvard Style

Kobs H., Krever H., Ebling D. and Trois C. (2024). SpectraNet: A Neural Network for Soybean Contents Prediction. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 502-509. DOI: 10.5220/0012697600003690

in Bibtex Style

@conference{iceis24,
author={Henry Kobs and Henrique Krever and Denilson Ebling and Celio Trois},
title={SpectraNet: A Neural Network for Soybean Contents Prediction},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={502-509},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012697600003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - SpectraNet: A Neural Network for Soybean Contents Prediction
SN - 978-989-758-692-7
AU - Kobs H.
AU - Krever H.
AU - Ebling D.
AU - Trois C.
PY - 2024
SP - 502
EP - 509
DO - 10.5220/0012697600003690
PB - SciTePress