Laser-Induced Breakdown Spectroscopy Associated with the Design of Experiments and Machine Learning for Discrimination of Brachiaria brizantha Seed Vigor

Cioccia, Guilherme; Morais, Carla Pereira de; Babos, Diego Victor; Milori, D. M. B. P.; Alves, Charline Zaratin; Cena, Cícero; Nicolodelli, Gustavo; Marangoni, Bruno

doi:10.3390/s22145067

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…Recent research in the literature has explored various computational methods that correlate image-derived parameters with seed quality standards [5,10,11]. Image analysis techniques provide non-destructive means to comprehend various aspects of seed development, establishing a connection between internal morphology and structural integrity [8,[12][13][14]. This approach enables the determination of the physiological potential of seed lots.…”

Section: Introductionmentioning

confidence: 99%

“…Cheng et al [7] combined low-field nuclear magnetic resonance (LF-NMR) spectral data and machine learning algorithms like Fisher's linear discriminant (FLD) to accurately distinguish high-and low-vigor rice seeds. Cioccia et al [12] used laser-induced breakdown spectroscopy (LIBS) and machine learning algorithms like linear discriminant analysis (LDA) to evaluate the vigor of Brachiaria brizantha seeds, achieving 100% accuracy in distinguishing high-and low-vigor seeds.…”

Section: Introductionmentioning

confidence: 99%

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Applying YOLOv8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds

da Silva,

de Freitas,

Abud

et al. 2024

AgriEngineering

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Seed quality significantly affects how well crops grow. Traditional methods for checking seed quality, like seeing how many seeds sprout or using a chemical test called tetrazolium testing, require people to look at the seeds closely, which takes a lot of time and effort. Nowadays, computer vision, a technology that helps computers see and understand images, is being used more in farming. Here, we use computer vision with X-ray imaging to assist experts in rapidly and accurately assessing seed quality. We looked at three different sets of seeds using X-ray images and used YOLOv8 to analyze them. YOLOv8 software measures different aspects about seeds, like their size and the area taken up by the part inside, called the endosperm. Based on this information, we put the seeds into four groups depending on how much endosperm they have. Our results show that the YOLOv8 program works well in identifying and separating the endosperm, even with a small amount of data. Our method was able to accurately identify the endosperm about 95.6% of the time. This means that our approach can help determine how effective the seeds are to plant crops.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying YOLOv8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds

da Silva,

de Freitas,

Abud

et al. 2024

AgriEngineering

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show abstract

“…In the research that focused on the identification of seed vigor of soybean and Brachiaria using LIBS, it was found that the presence of Ca elements in the seeds is the main characteristic responsible for the major variance in the data. Therefore, due to the fact that Ca elements play an important role in enzyme activities of plant during germination, LIBS was able to determine the seed vigor of soybean [26] and Brachiaria [27] by identifying Ca elements.…”

Section: Introductionmentioning

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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Probabilistic-based identification of gunshot residues (GSR) using Laser-Induced Breakdown Spectroscopy (LIBS) and Support Vector Machine (SVM) algorithm

Cioccia,

Wenceslau,

Ribeiro

et al. 2024

Microchemical Journal

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Laser-Induced Breakdown Spectroscopy Associated with the Design of Experiments and Machine Learning for Discrimination of Brachiaria brizantha Seed Vigor

Cited by 5 publications

References 31 publications

Applying YOLOv8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds

Applying YOLOv8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds

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

Probabilistic-based identification of gunshot residues (GSR) using Laser-Induced Breakdown Spectroscopy (LIBS) and Support Vector Machine (SVM) algorithm

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