FruitNet: Indian fruits image dataset with quality for machine learning applications

Meshram, Vishal; Patil, Kailas

doi:10.1016/j.dib.2021.107686

Cited by 32 publications

(5 citation statements)

References 8 publications

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“…The proposed hybrid model was applied to datasets previously studied in the literature, and the results were compared. Dataset C, the recommended dataset type (70% of the number of images is Train Set, 30% of the number of images is Test Set, And 20% of Train Set is Validation Set), was implemented on the FruitNet [25], Kesar Mango [26], and Papaya [27] dataset. When the performance of the hybrid model proposed in this study was compared with the performances of the studies in the literature, it was observed that the performance of the proposed hybrid model was better than the other models.…”

Section: F Results For Dataset Dmentioning

confidence: 99%

A Novel Deep Learning-Based Hybrid Method for the Determination of Productivity of Agricultural Products: Apple Case Study

Bal

Kayaalp

2023

IEEE Access

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The production of agricultural products and the high yield in these products are of critical importance for the continuation of human life. In recent years, machine learning and deep learning technologies have been widely used in determining agricultural productivity. The purpose of this study was to estimate the yield of apple fruit by using a novel deep learning-based hybrid method. First, by using images belonging to the golden and royal gala apple varieties, a classification was made with the help of a convolutional neural network (CNN) that was designed for the study. Then, using classical machine learning algorithms and bagging and boosting algorithms, a hybrid application was performed by classifying the images whose feature extractions were done with the designed CNN. The results of the study, presented on 4 separate datasets (Datasets A, B, C, and D), were evaluated based on accuracy, precision, recall, F-measure, and Cohen kappa scores. Considering the accuracy results for Datasets B, C, and D, it was determined that the hybrid model that gave the best result was the CNN-SVM model. For Dataset A, the CNN-SVM and CNN-Gradient Boosting hybrid models gave the best and same accuracy. Dataset C was determined as the most appropriate dataset in terms of the more balanced distribution of train, test, and validation size in the datasets, the results of the proposed hybrid CNN model, and the evaluation of the results of the model. For Dataset C, it was found that the accuracy of the hybrid model was 99.70%. Precision, recall, f-measure, and Cohen kappa scores were 99%. The results of the study revealed that the hybrid models showed effective results in determining the productivity of apple fruit through images belonging to the golden and royal gala varieties.INDEX TERMS Apple yield prediction, deep learning, ensemble methods, machine learning, smart farm.

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Section: F Results For Dataset Dmentioning

confidence: 99%

A Novel Deep Learning-Based Hybrid Method for the Determination of Productivity of Agricultural Products: Apple Case Study

Bal

Kayaalp

2023

IEEE Access

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“…Cannabis seed oil is used as an ingredient in body oils and lipid-enriched creams[4]. There are multiple datasets on fruits, vegetables [5 , 6 , 7] but there is a need of cannabis seed dataset for researchers to develop machine learning models and/or applications. This dataset contains the images of cannabis seeds and not their plants’ leaves.…”

Section: Data Descriptionmentioning

confidence: 99%

Dataset of cannabis seeds for machine learning applications

Chumchu

Patil²

2023

Data in Brief

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“…Banana plants (Musa spp.) [6] is a globally distributed fruit crop and is considered to be the largest herb [1] . It develops from the rhizome to form the stem, and the leaves grow larger.…”

Section: Data Descriptionmentioning

confidence: 99%