Prediction of leaf nitrogen in sugarcane (Saccharum spp.) by Vis-NIR-SWIR spectroradiometry

Fiorio, Peterson Ricardo; Silva, Carlos Augusto Alves Cardoso; Rizzo, Rodnei; Demattê, José Alexandre Melo; Luciano, Ana Cláudia dos Santos; Silva, Marcelo Andrade da

doi:10.1016/j.heliyon.2024.e26819

Cited by 6 publications

(1 citation statement)

References 59 publications

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“…In this context, spectroscopy has shown potential as a fast and non-invasive approach to observe plant characteristics, making it possible to monitor and optimize nitrogen fertilization through reflectance data, for example [11,12]. This method is based on the detection of biochemical changes that affect the photosynthetic activity, structure and stability of chemical bonds, which promotes changes in reflectance [13].…”

Section: Introductionmentioning

confidence: 99%

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

Tavares,

Silva,

Regazzo

et al. 2024

Preprint

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Beans are the most widely used protein source in the world and their productivity is directly linked to nitrogen (N). 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 prediction using NIR spectroscopy. Increasing doses of nitrogen were applied to the plants 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 VIP. Considering predictions with the whole NIR, the best results were achieved with RF (R2 = 0,84 and RMSE = 2,69 g kg-1) and KNN (R2 = 0,77 and RMSE = 3,86 g kg-1). The intervals of 700-740 nm and 983-995 nm were considered the most important for the study of N. More efficient predictions were verified when only spectral regions screened by VIP were included, increasing the accuracy of the RF, KNN and M5 models by 6%, 4% and 8%, respectively. The efficiency of N prediction based on NIR reflectance combined with machine learning was verified. This approach can optimize the management of nitrogen fertilization, serving as an important tool in precision agriculture.

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Section: Introductionmentioning

confidence: 99%

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

Tavares,

Silva,

Regazzo

et al. 2024

Preprint

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Prospects of artificial intelligence for the sustainability of sugarcane production in the modern era of climate change: An overview of related global findings

Bhatt,

Hossain,

Majumder

et al. 2024

Journal of Agriculture and Food Research

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Predictive modelling of chlorophyll in Mombaça grass leaves by hyperspectral reflectance data and machine learning

Sánchez,

Silva,

Demattê

et al. 2024

Grass and Forage Science

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Chlorophyll (Chl) concentration is one of the factors that affects crop productivity. This study investigated the prediction of chlorophyll concentrations in Mombaça grass' leaves using hyperspectral data and machine learning techniques. Chlorophyll variations were induced by different levels of nitrogen fertilization (104, 208, 312, and 416 kg ha−1). Spectral signatures (400–2500 nm) and chlorophyll contents of the leaves were obtained in October, November, and December 2017, and January 2018. Models were generated using Partial Least Square Regression (PLSR), Random Forest (RF), and Support Vector Regression (SVR). Two validation techniques were employed: holdout, dividing the data into training (75%) and testing (25%) sets; and leave‐one‐date‐out cross‐validation (LOOCV), in which one date was omitted during model training and used to predict the omitted date's value. Chlorophyll concentrations varied according to N doses, with the highest concentrations observed in October and December. In these months, there were greater variations in spectral reflectance in the green and red bands (530–680 nm). December was identified as the ideal period for chlorophyll quantification, for both holdout and LOOCV validation techniques. The SVR technique performed best (R2 = 0.71, RMSE = 0.23 mg g−1, dr = 0.72) compared to RF (R2 = 0.63, RMSE = 0.27 mg g−1, dr = 0.66) and PLSR (R2 = 0.60, RMSE = 0.27 mg g−1, dr = 0.67). Therefore, the prediction of chlorophyll in Mombaça grass using spectroradiometry is promising and applicable across different cultivation periods.

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Prediction of leaf nitrogen in sugarcane (Saccharum spp.) by Vis-NIR-SWIR spectroradiometry

Cited by 6 publications

References 59 publications

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

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

Prospects of artificial intelligence for the sustainability of sugarcane production in the modern era of climate change: An overview of related global findings

Predictive modelling of chlorophyll in Mombaça grass leaves by hyperspectral reflectance data and machine learning

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