VegNet: Dataset of vegetable quality images for machine learning applications

Suryawanshi, Yogesh Chandrakant; Patil, Kailas; Chumchu, Prawit

doi:10.1016/j.dib.2022.108657

Cited by 20 publications

(12 citation statements)

References 10 publications

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“…In the realm of machine learning datasets, several notable contributions have emerged recently [2] , [3] , [4] , [5] , [6] , [7] , [8] , 10] catering to machine learning applications. We wanted to show just how valuable our Thai Cannabis Plant Dataset [1 , 9] is, so we ran some experiments using well-known pre-trained models like VGG19, DenseNet201, and EfficientNetB7.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

A comprehensive dataset of eight Thai cannabis classes for botanical exploration

Patil,

Chumchu

2024

Data in Brief

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

A comprehensive dataset of eight Thai cannabis classes for botanical exploration

Patil,

Chumchu

2024

Data in Brief

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“…Machine learning is a branch of Artificial Intelligence (Garrido, 2010) The dataset used in this research uses a public dataset, because the local rice dataset is not as large as the number of rice image datasets (Suryawanshi et al, 2022), (Özkan, 2022) available in public. The research deliberately uses a rather large image dataset (Mavromatis et al, 2022) of around 15,000 image datasets.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Convolutional Neural Network and Artificial Neural Network for Rice Detection

Suherman

Hindarto

Makmur

et al. 2023

SinkrOn

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Rice is a staple food for people in tropical countries. Indonesia is a country that needs a lot of rice for its people in providing food. This country has implemented various ways to plant rice properly. Many agricultural fields have implemented harvests up to three times a year, due to the role of technology which has helped a lot in agriculture. Planting to harvest already uses advanced technology and tools. A good rice harvest can improve the welfare of the surrounding community. Meanwhile with lots of rice products because many rice plants produce with lots of rice. The type of rice from different regions of origin, the yield of rice is also different from other regions of origin. But with advances in technology, it is possible to plant rice whose types of plants come from other regions. The rice sold to the public varies, so that people who are unfamiliar with the types of rice find it difficult to detect the types of rice. Machine learning is present in detecting various kinds of rice. Machine learning, especially deep learning can make better detection, because one of the deep learning methods works similar to the human brain. In the human brain there are millions or even billions of neurons. This research uses neural networks in experiments using public datasets. Experiments using Artificial Neural Networks achieve an training accuracy of 98.2%, loss: 0.2351. It takes about 10 minutes of training. Testing accuracy reaches accuracy: 96%, loss: 0.6641. By conducting experiments using the Convolution Neural Network, it achieves an accuracy of 99.3% and the training time requires around 18 hours. The purpose of this research is to classify the rice image dataset and detect the rice image.

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“…The image datasets play a crucial role in various fields, ranging from computer vision and machine learning to medical research and social sciences [1] . These datasets provide a rich source of visual information that enables researchers, developers, and professionals to train and validate their models, algorithms, and theories.…”

Section: Data Descriptionmentioning

confidence: 99%

Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications

Thite,

Suryawanshi,

Patil

et al. 2024

Data in Brief

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VegNet: Dataset of vegetable quality images for machine learning applications

Cited by 20 publications

References 10 publications

A comprehensive dataset of eight Thai cannabis classes for botanical exploration

A comprehensive dataset of eight Thai cannabis classes for botanical exploration

Comparison of Convolutional Neural Network and Artificial Neural Network for Rice Detection

Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications

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