Capability of short Vis-NIR band tandem with machine learning to rapidly predict NPK content in tropical farmland: A case study of Aceh Province agricultural soil dry land, Indonesia

Mustaqimah,; Devianti,; Munawar, Agus Arip; Sufardi, Sufardi

doi:10.1016/j.cscee.2024.100711

Case Studies in Chemical and Environmental Engineering

2024

DOI: 10.1016/j.cscee.2024.100711

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Capability of short Vis-NIR band tandem with machine learning to rapidly predict NPK content in tropical farmland: A case study of Aceh Province agricultural soil dry land, Indonesia

Mustaqimah,

Devianti,

Agus Arip Munawar

et al.

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2024

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Cited by 2 publications

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Predictive soil nutrient modeling with spectral data and machine learning in four major Indonesian Provinces located on the island of java

Ramdhani,

Adnan,

Suryana

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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This study provides a detailed analysis of predicting soil nutrient content using spectral data and machine learning techniques in four Indonesian provinces: West Java, Central Java, Yogyakarta (DIY), and East Java. The research collected 145 soil samples to predict various key soil nutrients, such as N Total, NH4, NO3, P Total, P Available, K Total, K Available, C Organic, and pH. The study used linear regression (LR) and deep neural networks (DNN) with a deep cross-network (DCN) architecture to model the relationships between soil spectral data and nutrient content. LR was used as a baseline model to understand linear relationships between spectral features and soil properties and identify the most influential spectral frequencies in predicting soil nutrient levels. On the other hand, the DNN model captured complex, non-linear patterns within the data. Results showed that while the DNN model displayed advanced capabilities, the LR model generally outperformed it in predictive accuracy, particularly for nutrients like N-Total, P-Total, and K-Total. The findings highlight the potential of combining spectral data with advanced machine-learning techniques for precise soil nutrient estimation, which could significantly enhance agricultural productivity and soil management practices in Indonesia.

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Predictive soil nutrient modeling with spectral data and machine learning in four major Indonesian Provinces located on the island of java

Ramdhani,

Adnan,

Suryana

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Performance of Machine Learning Models in Predicting Common Bean (Phaseolus vulgaris L.) Crop Nitrogen Using NIR Spectroscopy

Tavares,

Silva,

Regazzo

et al. 2024

Agronomy

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Beans are the main direct source of protein consumed by humans in the world and their productivity is directly linked to nitrogen. The short crop cycle imposes the need for fast methodologies for N quantification. In this work, we evaluated the performance of four machine learning algorithms in nitrogen estimation using NIR spectroscopy, comparing predictions between complete spectral data and only intervals obtained with the variable importance in projection (VIP). Doses of 0, 50, 100, and 150 kg ha−1 of N were applied and leaf reflectance was collected. Weka software was used to test the algorithms. The selection of the most effective spectral zones was made with the variable importance in projection (VIP). The intervals of 700–740 nm and 983–995 nm were considered the most important for the study of nitrogen. More efficient predictions were verified for RF and KNN models (R2 = 0.89, RMSE = 2.23 g kg−1; and R2 = 0.80, RMSE = 2.89 g kg−1, respectively) when only the most important spectral regions were included. The efficiency of nitrogen prediction based on NIR reflectance combined with machine learning was verified, which can serve as an important tool in precision agriculture.

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Capability of short Vis-NIR band tandem with machine learning to rapidly predict NPK content in tropical farmland: A case study of Aceh Province agricultural soil dry land, Indonesia

Cited by 2 publications

References 32 publications

Predictive soil nutrient modeling with spectral data and machine learning in four major Indonesian Provinces located on the island of java

Predictive soil nutrient modeling with spectral data and machine learning in four major Indonesian Provinces located on the island of java

Performance of Machine Learning Models in Predicting Common Bean (Phaseolus vulgaris L.) Crop Nitrogen Using NIR Spectroscopy

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