Processamento de imagens como ferramenta importante para inteligência artificial no setor de sementes

Pinheiro, Romário de Mesquita; Gadotti, Gizele Ingrid; Bernardy, Ruan; Monteiro, Rita de Cassia Mota; Moreira, Isabella Brandão

doi:10.32406/v5n1/2022/89-101/agrariacad

Cited by 3 publications

(1 citation statement)

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“…Other studies, such as Gadotti et al (2022aGadotti et al ( , 2022b, Medeiros et al (2020b), Pinheiro et al (2022Pinheiro et al ( , 2021, have also highlighted the use of artificial intelligence in the seed sector. Producers require decision-making tools for allocating higher-quality seeds, as mandated by Brazilian seed legislation.…”

Section: Attributementioning

confidence: 99%

Computer vision by unsupervised machine learning in seed drying process

Pinheiro,

Gadotti,

Bernardy

et al. 2023

Ciênc. agrotec.

Self Cite

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Analyzing the impact of harvest-time drying data is crucial for successful storage and maintaining regulatory seed quality. This study aimed to assess the performance of fixed and mobile dryers using machine learning techniques. Data were collected from convective dryers, including the total number of dryers used, drying time (in hours), moisture percentages at the product’s entrance and exit, and the humidity difference between them. The study employed the Filtered Clusterer model, which utilizes the Simple K-Means technique and the Resample filter to group data based on similarities. The findings indicated distinct differences between fixed and mobile drying systems, with well-defined variations within each system. The algorithm, combined with the applied filters, proved effective in unsupervised classification by identifying and reducing inter-cluster similarity within the fixed system, thereby creating distinct classes within the dataset. In conclusion, the algorithm successfully clustered the scattered dataset and accurately classified and minimized inter-cluster similarity within the fixed system. Conversely, the mobile system exhibited low drying efficiency.

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Section: Attributementioning

confidence: 99%

Computer vision by unsupervised machine learning in seed drying process

Pinheiro,

Gadotti,

Bernardy

et al. 2023

Ciênc. agrotec.

Self Cite

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Artificial intelligence tools and a diagrammatic scale for evaluating the quality of coating in treated soybean seeds

de Andrade,

Carvalho,

Pires

et al. 2023

Neural Comput & Applic

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Identifying soybean genotypes with artificial intelligence and near infrared reflectance

Bernardy,

Cañizares,

Meza

et al. 2024

Ciênc. agrotec.

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With the increasing soybean production in Brazil, and the demand for soybeans with high protein and oil content, it is essential to conduct an in-depth study of the constituents of this grain, which can vary according to genotypes and growing conditions. Therefore, the objective of this study was to classify soybean genotypes, cultivated in different environments and sowing seasons, according to their chemical composition and the spectrum generated by near-infrared spectroscopy (NIRS). For this purpose, artificial intelligence and its machine learning technique were employed. 10 soybean genotypes were used, sown in two sowing seasons and cultivated 7 cities in Rio Grande do Sul. The chemical composition of the samples was analyzed using the FOSS NIRS DS2500 equipment, selecting the band between 807 and 817 nm. The applied algorithms were J48, Random Forest, CVR, lBk, MLP, using the Resample filter. The Weka software, version 3.8.6, was employed for data mining. The IBk algorithm achieved the best performance, reaching 89% correct classification of attributes. From the Confusion Matrix, it was observed that all genotypes obtained results above 60/70 for correctly predicted values, highlighting the algorithms’ good performance. In the metrics, IBk achieved 0.89 Precision, Recall, and F-Measure, and 0.94 ROC Area. Thus, it was possible to classify the genotypes according to their chemical composition related to the data obtained in the spectral curve, sowing season, and environment, using artificial intelligence and machine learning.

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Processamento de imagens como ferramenta importante para inteligência artificial no setor de sementes

Cited by 3 publications

References 0 publications

Computer vision by unsupervised machine learning in seed drying process

Computer vision by unsupervised machine learning in seed drying process

Artificial intelligence tools and a diagrammatic scale for evaluating the quality of coating in treated soybean seeds

Identifying soybean genotypes with artificial intelligence and near infrared reflectance

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