Application of Machine Learning for Analysis of Fruit Defect: A Review

Tulli, Siddharth; Yogesh, .

doi:10.1007/978-981-19-7346-8_45

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Previous studies have shown a growing interest in deep learning-based methods for fruit ripeness detection due to their ability to automatically learn and extract complex features from large-scale datasets [10]. Deep learning models, such as Convolutional Neural Networks (CNNs), have demonstrated remarkable performance in various computer vision tasks, including object recognition and image classification and other related applications [11,12,19]. Researchers have adopted deep learning approaches to develop robust and accurate models for fruit ripeness detection, overcoming some of the limitations of traditional image processing techniques.…”

Section: Introductionmentioning

confidence: 99%

A Fruit Ripeness Detection Method using Adapted Deep Learning-based Approach

Zhang

2023

IJACSA

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Fruit ripeness detection plays a crucial role in precise agriculture, enabling optimal harvesting and post-harvest handling. Various methods have been investigated in the literature for fruit ripeness detection in vision-based systems, with deep learning approaches demonstrating superior accuracy compared to other approaches. However, the current research challenge lies in achieving high accuracy rates in deep learningbased fruit ripeness detection. In this study proposes a method based on the YOLOv8 algorithm to address this challenge. The proposed method involves generating a model using a custom dataset and conducting training, validation, and testing processes. Experimental results and performance evaluation demonstrate the effectiveness of the proposed method in achieving accurate fruit ripeness detection. The proposed method surpasses existing approaches through extensive experiments and performance analysis, providing a reliable solution for fruit ripeness detection in precise agriculture.

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Section: Introductionmentioning

confidence: 99%

A Fruit Ripeness Detection Method using Adapted Deep Learning-based Approach

Zhang

2023

IJACSA

View full text Add to dashboard Cite

show abstract

Application of Machine Learning for Analysis of Fruit Defect: A Review

Cited by 1 publication

References 7 publications

A Fruit Ripeness Detection Method using Adapted Deep Learning-based Approach

A Fruit Ripeness Detection Method using Adapted Deep Learning-based Approach

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