Inteligência artificial na agricultura com aplicabilidade no setor sementeiro

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

doi:10.48017/diversitas_journal-v6i3-1857

Cited by 9 publications

(9 citation statements)

References 6 publications

(6 reference statements)

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“…Outro setor em que ela pode ser aplicada é no agrícola, sendo que ela pode otimizar o plantio, monitorar o crescimento das culturas e prever colheitas com base em dados meteorológicos, informações do solo e outros fatores, portanto ela está revolucionando a agricultura, tornando-a mais eficiente e sustentável por meio de sua análise e compilação de dados (Pinheiro, et al 2021), assim como nas redes sociais, na qual ela é responsável pela recomendação de conteúdo, personalização de feeds e identificação de comportamentos suspeitos, como discurso de ódio ou fake News (Kaufman;Santaella, 2020).…”

Section: A Inteligência Artificial E Os Dadosunclassified

Aplicações De Inteligência Artificial Na Otimização De Banco De Dados

Menezes,

Barbosa,

Garzon

2023

RECIMA21

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Com o avanço da Internet e a expansão da Internet das Coisas (IoT), a eficiente gestão de bancos de dados tornou-se essencial para empresas e instituições, tendo isso em vista, esse artigo tem como objetivo demonstrar como a Inteligência Artificial (IA) pode otimizar a gestão de bancos de dados, explorando seu potencial na melhoria dos sistemas de gerenciamento de dados, para isso foi realizado uma pesquisa bibliográfica, permitindo uma análise sistemática e crítica da literatura disponível sobre o assunto, por meio da qual ficou evidente que a otimização de bancos de dados é essencial, dada a crescente complexidade e volume de informações geradas e que a IA desempenha um papel fundamental nesse contexto, possibilitando a análise de padrões de uso, otimização semântica de consultas, aprimoramento do armazenamento e recuperação de informações tridimensionais, bem como a previsão de falhas e a manutenção preditiva e também na segurança do sistema.

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Section: A Inteligência Artificial E Os Dadosunclassified

Aplicações De Inteligência Artificial Na Otimização De Banco De Dados

Menezes,

Barbosa,

Garzon

2023

RECIMA21

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“…One of the tools used with some frequency in several sectors of agriculture is Machine Learning (ML), which is considered an artificial intelligence that identifies similar patterns of the object of study, consisting in the automation of data analysis, without the demand for new continuous programming in the equipment or software (Pinheiro et al, 2021). ML is used in conjunction with remote sensing data, which has enabled the application in new areas and methodologies for both tools (Scheunders et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Multispectral images for discrimination of sources and doses of fertilizer in coffee plants

et al. 2023

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Remote monitoring of the management of coffee crops is necessary as the demand in decision-making, where the aim is to rise production based on sustainable management is in a constant growth. In this work, it was evaluated the potential of images obtained by low-cost sensors in the discrimination of sources and doses of mineral and organomineral fertilizers in coffee. The experimental design was in randomized blocks, with five blocks and six treatments, as follows: (T1) -100% of the organomineral treatment; (T2) -70% of the organomineral treatment; (T3) -50% of the organomineral treatment;(T4) -100% of mineral fertilization; (T5) -standard treatment of the farm and (T6) -70% of mineral fertilization.After management, we used the Mapir 3 Survey3W camera coupled to an ARP drone -Phantom4 to take images of the experiment over a 12-month vegetative period. Combined with image taking, it was collected agronomic parameters of coffee growth and productivity for two crops and concluded that different fertilization doses did not significantly affect the analyzed parameters. Based on the supervised classification of multispectral images, it was possible to discriminate treatments with a higher degree of accuracy (86.66% accuracy) than when analyzing coffee growth parameters.

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“…The use of information technology in the seed sector can improve results generated by seed quality tests, resulting in fast responses for decision-making in the planting, application of inputs, and harvest and post-harvest processes, leading to a more intelligent agriculture (PINHEIRO et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…The ranking lots into high, medium, and low vigor are essential for all seed companies to speed up the delivery of standardized quality seeds to producers, as well as seed mapping regions to be grown, through results of physiological quality tests. In this sense, the demand for efficient and safe methods is increasing, and the information technology is a support tool for the seed sector, as the combination of adequate information regarding time and careful decisionmaking, which are essential for the success of this market (PINHEIRO et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Data mining for ranking sorghum seed lots

et al. 2023

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The ranking of seed lots is a fundamental process for all companies in the seed industry. This work aims to demonstrate data mining methods for ranking sorghum seed lots during the seed processing through analysis of quality control data. Germination and cold tests were performed to verify the physiological quality of the lots. Seed samples from each lot were evaluated in two moments: post-cleaning and finished product (ready for marketing). The results after pre-processing totaled 188 rows of data with six attributes, encompassing 150 lots accepted for marketing, 6 rejected, and 32 intermediate lots. The classifiers used were J48, Random Forest, Classification Via Regression, Naive Bayes, Multilayer Perceptron, and IBk. The Resample filter was used for adjustment of the data. The k-fold technique was used for training, with ten folds. The metrics of Accuracy, Precision, Recall, F-measure, and ROC Area were used to verify the accuracy of the algorithms. The results obtained were used to determine the best machine-learning algorithm. IBk and J48 presented the highest accuracy of data; the IBk technique presented the best results. The Resample filter was essential for solving the data imbalance problem. Sorghum seed lots can be classified with great accuracy and precision through artificial intelligence and machine learning technique.

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Inteligência artificial na agricultura com aplicabilidade no setor sementeiro

Abstract: Todo o conteúdo expresso neste artigo é de inteira responsabilidade dos seus autores.

Cited by 9 publications

References 6 publications

Aplicações De Inteligência Artificial Na Otimização De Banco De Dados

Aplicações De Inteligência Artificial Na Otimização De Banco De Dados

Multispectral images for discrimination of sources and doses of fertilizer in coffee plants

Data mining for ranking sorghum seed lots

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