The Impact of Cloud Computing and Artificial Intelligence in Digital Agriculture

Dozono, Kohei; Amalathas, Sagaya Sabestinal; Saravanan, Ravan

doi:10.1007/978-981-16-2377-6_52

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…However, we may train intelligent systems, neural networks, and machine learning systems to deal with this variability ( Lopes et al., 2021 ; Taylor et al., 2020 ). Intelligent systems like neural networks learn, and this enables the optimisation of the operational characteristics of different agricultural equipment or systems ( Batiha and Krömer, 2021 ; Dozono et al., 2022 ; Kamyshova et al., 2022 ). Emerging or declining themes …”

Section: Resultsmentioning

confidence: 99%

A bibliometric and thematic approach to agriculture 4.0

Mühl

Oliveira

2022

Heliyon

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Section: Resultsmentioning

confidence: 99%

A bibliometric and thematic approach to agriculture 4.0

Mühl

Oliveira

2022

Heliyon

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“…So digital image processing technology is used to determine the ripeness level of the papaya fruit with better precision and efficiency. This research is to create an artificial intelligence model for papaya farmers in determining the maturity level of California papaya (Carica Papaya L) when the harvesting process is carried out (K. Dozono, S. Amalathas, 2022).…”

Section: Introductionmentioning

confidence: 99%

Classification for Papaya Fruit Maturity Level With Convolutional Neural Network

Nurmalasari,

Setiawan,

Astuti

et al. 2023

J. Ris. Inform.

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Papaya California (Carica papaya L) is one of the agricultural commodities in the tropics and has a very big opportunity to develop in Indonesia as an agribusiness venture with quite promising prospects. So the quality of papaya fruit is determined by the level of maturity of the fruit, the hardness of the fruit, and its appearance. Papaya fruit undergoes a marked change in color during the ripening process, which indicates chemical changes in the fruit. The change in papaya color from green to yellow is due to the loss of chlorophyll. The papaya fruit is initially green during storage, then turns slightly yellow. The longer the storage color, the changes to mature the yellow. The process of classifying papaya fruit's ripeness level is usually done manually by business actors, that is, by simply looking at the color of the papaya with the normal eye. Based on the problems that exist in classifying the ripeness level of papaya fruit, in this research, we create a system that can be used to classify papaya fruit skin color using a digital image processing approach. The method used to classify the maturity level of papaya fruit is the Convolutional Neural Network (CNN) Architecture to classify the texture and color of the fruit. This study uses eight transfer learning architectures with 216 simulations with parameter constraints such as optimizer, learning rate, batch size, number of layers, epoch, and dense and can classify the ripeness level of the papaya fruit with a fairly high accuracy of 97%. Farmers use the results of the research in classifying papaya fruit to be harvested by differentiating the maturity level of the fruit more accurately and maintaining the quality of the papaya fruit.

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