Fast and Non-Destructive Determination of N, P, and K in Sorghum, Oat, and Corn Residue Using Near-Infrared Spectroscopy

Savi, A.; Aguiar, Lana Maria de; Tonial, Larissa M. S.; Lafay, Cíntia Boeira Batista; Assmann, Tangriani Simioni; Bortolli, Marcos Antonio de

doi:10.5539/jas.v11n13p304

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…The alterations in the 860-910 nm indicate the presence of CH and OH bonds in carbohydrates [81]. Furthermore, changes in the 930-1030 nm range were explained by the presence of protein compounds in the sample [82]. The presence of each of these substances in the seed composition increases the probability of germination, highlighting the importance of their detection [78].…”

Section: Examination Of Averages Of Vis/nir Absorption Spectra and Ev...mentioning

confidence: 99%

Identifying Optimal Wavelengths from Visible–Near-Infrared Spectroscopy Using Metaheuristic Algorithms to Assess Peanut Seed Viability

Rajabi-Sarkhani,

Abbaspour-Gilandeh,

Moinfar

et al. 2023

Agronomy

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Peanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation capacity make them an appropriate choice for cultivation in adverse environmental conditions. The germination ability of seeds profoundly impacts the final yield of the crop; assessing seed viability is of extreme importance. Conventional methods for assessing seed viability and germination are both time-consuming and costly. To address these challenges, this study investigated Visible–Near-Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500–1030 nm as a nondestructive and rapid method to determine the viability of two varieties of peanut seeds: North Carolina-2 (NC-2) and Spanish flower (Florispan). The study subjected the seeds to three levels of artificial aging through heat treatment, involving incubation in a controlled environment at a relative humidity of 85% and a temperature of 50 °C over 24 h intervals. The absorbance spectra noise was significantly mitigated and corrected to a large extent by combining the Savitzky–Golay (SG) and multiplicative scatter correction (MSC) methods. To identify the optimal wavelengths for seed viability assessment, a range of metaheuristic algorithms were employed, including world competitive contest (WCC), league championship algorithm (LCA), genetics (GA), particle swarm optimization (PSO), ant colony optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization (HTS), forest optimization (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK). These algorithms offer powerful optimization capabilities for effectively extracting relevant wavelength information from spectral data. Results revealed that all the algorithms demonstrated remarkable accuracy in predicting the allometric coefficient of seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036, respectively. In terms of execution time, the ICA (2.3635 s) and LCA (44.9389 s) algorithms exhibited the most and least efficient performance, respectively. Conversely, the FOA and the LCA algorithms excelled in identifying the least number of optimal wavelengths (10 wavelengths). Subsequently, the seeds were classified based on the wavelengths selected via the FOA (10 wavelengths) and (DSOS (16 wavelengths) methods, in conjunction with logistic regression (LR), decision tree (DT), multilayer perceptron (MP), support vector machine (SVM), k-nearest neighbor (K-NN), and naive Bayes (NB) classifiers. The DSOS–DT and FOA–MP methods demonstrated the highest accuracy, yielding values of 0.993 and 0.983, respectively. Conversely, the DSOS–LR and DSOS–KNN methods obtained the lowest accuracy, with values of 0.958 and 0.961, respectively. Overall, our findings demonstrated that Vis/NIR spectroscopy, coupled with variable selection algorithms and learning methods, presents a suitable and nondestructive approach for detecting seed viability.

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Section: Examination Of Averages Of Vis/nir Absorption Spectra and Ev...mentioning

confidence: 99%

Identifying Optimal Wavelengths from Visible–Near-Infrared Spectroscopy Using Metaheuristic Algorithms to Assess Peanut Seed Viability

Rajabi-Sarkhani,

Abbaspour-Gilandeh,

Moinfar

et al. 2023

Agronomy

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show abstract

“…grapes, avocados, or mangoes), forage quality analysis, and soil analysis (Peng et al, 2015;Tang, Jones & Minasny, 2020;Ng et al, 2019). There are few published results in the literature that have explored the use of NIRS for the nutrient analysis of various plants such as mustards (Martínez-Valdivieso, Font & Río-Celestino, 2019), sorghum, oat, and corn (Savi et al, 2019), wheat and barley (Zerner & Parker, 2019), vine and grape berries (Cuq et al, 2020), and peach (Dedeoglu, 2020). The use of NIRS for the determination of plant nutrient status in cotton is limited and was reported in a study by Tarpley, Reddy & Sassenrath-Cole (2000) in the United States of America.…”

Section: Introductionmentioning

confidence: 99%

Rapid and cost-effective nutrient content analysis of cotton leaves using near-infrared spectroscopy (NIRS)

Prananto

Minasny

Weaver

2021

PeerJ

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The development of portable near-infrared spectroscopy (NIRS) combined with smartphone cloud-based chemometrics has increased the power of these devices to provide real-time in-situ crop nutrient analysis. This capability provides the opportunity to address nutrient deficiencies early to optimise yield. The agriculture sector currently relies on results delivered via laboratory analysis. This involves the collection and preparation of leaf or soil samples during the growing season that are time-consuming and costly. This delays farmers from addressing deficiencies by several weeks which impacts yield potential; hence, requires a faster solution. This study evaluated the feasibility of using NIRS in estimating different macro- and micronutrients in cotton leaf tissues, assessing the accuracy of a portable handheld NIR spectrometer (wavelength range of 1,350–2,500 nm). This study first evaluated the ability of NIRS to predict leaf nutrient levels using dried and ground cotton leaf samples. The results showed the high accuracy of NIRS in predicting essential macronutrients (0.76 ≤ R2 ≤ 0.98 for N, P, K, Ca, Mg and S) and most micronutrients (0.64 ≤ R2 ≤ 0.81 for Fe, Mn, Cu, Mo, B, Cl and Na). The results showed that the handheld NIR spectrometer is a practical option to accurately measure leaf nutrient concentrations. This research then assessed the possibility of applying NIRS on fresh leaves for potential in-field applications. NIRS was more accurate in estimating cotton leaf nutrients when applied on dried and ground leaf samples. However, the application of NIRS on fresh leaves was still quite accurate. Using fresh leaves, the prediction accuracy was reduced by 19% for macronutrients and 11% for micronutrients, compared to dried and ground samples. This study provides further evidence on the efficacy of using NIRS for field estimations of cotton nutrients in combination with a nutrient decision support tool, with an accuracy of 87.3% for macronutrients and 86.6% for micronutrients. This application would allow farmers to manage nutrients proactively to avoid yield penalties or environmental impacts.

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“…NIRS has been used extensively in the agriculture industry, including grain quality analysis, dry matter or sugar analysis of fruits (e.g., grapes, avocados, or mangoes), forage quality analysis, and soil analysis (e.g., Peng et al, 2015;Tang et al, 2020;Ng et al, 2019). There are few published results in the literature that have explored the use of NIRS for the nutrient analysis of various plants such as mustards (Martínez-Valdivieso et al, 2019), sorghum, oat, and corn (Savi et al, 2019), wheat and barley (Zerner and Parker, 2019), vine and grape berries (Cuq et al, 2019), andpeach (Dedeoglu, 2020). The use of NIRS for the determination of plant nutrient status in cotton is limited and was reported in a study by Tarpley et al (2000) in the United States of America.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Rapid and cost-effective nutrient content analysis of cotton leaves using near-infrared spectroscopy (NIRS) (v0.1)"

2021

View full text Add to dashboard Cite

The development of portable near-infrared spectroscopy (NIRS) combined with smartphone cloud-based chemometrics has increased the power of these devices to provide real-time in-situ crop nutrient analysis. This capability provides the opportunity to address nutrient deficiencies early to optimise yield. The agriculture sector currently relies on results delivered via laboratory analysis. This involves the collection and preparation of leaf or soil samples during the growing season that are time-consuming and costly. This delays farmers from addressing deficiencies by several weeks which impacts yield potential; hence, requires a faster solution. This study evaluated the feasibility of using NIRS in estimating different macro-and micronutrients in cotton leaf tissues, assessing the accuracy of a portable handheld NIR spectrometer (wavelength range of 1350-2500 nm).This study first evaluated the ability of NIRS to predict leaf nutrient levels using dried and ground cotton leaf samples. The results showed the high accuracy of NIRS in predicting essential macronutrients (0.76 ≤ R 2 ≤ 0.98 for N, P, K, Ca, Mg, and S) and most micronutrients (0.64 ≤ R 2 ≤ 0.81 for Fe, Mn, Cu, Mo, B, Cl, and Na). The results showed that the handheld NIR spectrometer is a practical option to accurately measure leaf nutrient concentrations. This research then assessed the possibility of applying NIRS on fresh leaves for potential in-field applications. NIRS was more accurate in estimating cotton leaf nutrients when applied on dried and ground leaf samples. However, the application of NIRS on fresh leaves was still quite accurate. Using fresh leaves, the prediction accuracy was reduced by 19% for macronutrients and 11% for micronutrients, compared to dried and ground samples. This study provides further evidence on the efficacy of using NIRS for field estimations of cotton nutrients in combination with a nutrient decision support tool, with an accuracy of 87.3% for macronutrients and 86.6% for micronutrients. This application would allow farmers to manage nutrients proactively to avoid yield penalties or environmental impacts.

show abstract

Fast and Non-Destructive Determination of N, P, and K in Sorghum, Oat, and Corn Residue Using Near-Infrared Spectroscopy

Cited by 6 publications

References 28 publications

Identifying Optimal Wavelengths from Visible–Near-Infrared Spectroscopy Using Metaheuristic Algorithms to Assess Peanut Seed Viability

Identifying Optimal Wavelengths from Visible–Near-Infrared Spectroscopy Using Metaheuristic Algorithms to Assess Peanut Seed Viability

Rapid and cost-effective nutrient content analysis of cotton leaves using near-infrared spectroscopy (NIRS)

Peer Review #1 of "Rapid and cost-effective nutrient content analysis of cotton leaves using near-infrared spectroscopy (NIRS) (v0.1)"

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