Orange Fruit Recognition Using Neural Networks

Singh, Surya Pratap; Shivam,; Kumar, Indrajeet

doi:10.1007/978-981-16-6893-7_38

Cited by 4 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning continues to play a crucial role in enhancing the quality of life and creating smarter solutions for challenges in daily life and industry [31] [32]. Image recognition is also bringing significant improvements to agriculture, enhancing efficiency and production quality [33].…”

Section: Introductionmentioning

confidence: 99%

Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques

Huong,

Hien,

Son

et al. 2024

Preprint

View full text Add to dashboard Cite

This study introduces significant improvements in the construction of Deep Convolutional Neural Network (DCNN) models for classifying agricultural products, specifically oranges, based on their shape, size, and color. Utilizing the MobileNetV2 architecture, this research leverages its efficiency and lightweight nature, making it suitable for mobile and embedded applications. Key techniques such as Depthwise Separable Convolutions, Linear Bottlenecks, and Inverted Residuals help reduce the number of parameters and computational load while maintaining high performance in feature extraction. Additionally, the study employs comprehensive data augmentation methods, including horizontal and vertical flips, grayscale transformations, hue adjustments, brightness adjustments, and noise addition to enhance the model's robustness and generalization capabilities. The proposed model demonstrates superior performance, achieving an overall accuracy of 100% with nearly perfect precision, recall, and F1-score for both "orange_good" and "orange_bad" classes, significantly outperforming previous models which typically achieved accuracies between 70–90%. The confusion matrix shows that the model has high sensitivity and specificity, with very few misclassifications. Finally, this study empresentasizes the practical applicability of the proposed model, particularly its easy deployment on resource-constrained devices and its effectiveness in agricultural product quality control processes. These findings affirm the model in this research as a reliable and highly efficient tool for agricultural product classification, surpassing the capabilities of traditional models in this field.

show abstract

Section: Introductionmentioning

confidence: 99%

Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques

Huong,

Hien,

Son

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Music Player Based on Emotion Detection Using CNN

Arya

Bhatt

Mittal³

2022

2022 IEEE North Karnataka Subsection Flagship International Conference (NKCon)

View full text Add to dashboard Cite

A Comprehensive Review: Advancements in Pretrained and Deep Learning Methods in the Disease Detection of Rice Plants

Arya¹,

Mishra²

2023

JAICN

View full text Add to dashboard Cite

Rice cultivation is essential to the global economy, particularly in India, where it holds the distinction of being the largest rice exporter and the second-largest rice producer. However, the agricultural sector faces significant challenges due to diseases and pests that negatively impact the crops, by hindering the plant growth, reducing the yield, and, in extreme cases, leading to famine. The use of pesticides, intended to increase production, often results in a decline in crop quality. Prompt as well as precise disease identification in plants is requisite for prevention and control of disease, enabling timely implementation of pesticide control measures. This has spurred research at the intersection of computer science and agriculture, specifically focused on identifying diseases in rice through collected and real-time images. Deep learning (DL) has emerged as a key area of study within this domain, addressing various aspects of agricultural plant protection, including disease detection and pest control. Pretrained models have proven to be invaluable tools in the realm of rice plant disease identification and monitoring. These models leverage transfer learning, enhance feature extraction, reduce training time and resource requirements, improve generalization and resilience, and facilitate knowledge sharing and collaboration. This article examines rice plant diseases, explores deep learning and pre-trained models for diagnosis, reviews relevant publications, and presents a comparative analysis of research studies to assess advancements in rice plant disease detection.

show abstract

Orange Fruit Recognition Using Neural Networks

Cited by 4 publications

References 13 publications

Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques

Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques

Music Player Based on Emotion Detection Using CNN

A Comprehensive Review: Advancements in Pretrained and Deep Learning Methods in the Disease Detection of Rice Plants

Contact Info

Product

Resources

About