Fruits and vegetables preservation based on AI technology: Research progress and application prospects

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 226, P. 109382 - 109382

Published: Aug. 27, 2024

Language: Английский

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Machine vision combined with deep learning–based approaches for food authentication: An integrative review and new insights

Comprehensive Reviews in Food Science and Food Safety, Journal Year: 2024, Volume and Issue: 23(6)

Published: Nov. 1, 2024

Food fraud undermines consumer trust, creates economic risk, and jeopardizes human health. Therefore, it is essential to develop efficient technologies for rapid reliable analysis of food quality safety authentication. Machine vision-based methods have emerged as promising solutions the nondestructive authenticity quality. The Industry 4.0 revolution has introduced new trends in this field, including use deep learning (DL), a subset artificial intelligence, which demonstrates robust performance generalization capabilities, effectively extracting features, processing extensive data. This paper reviews recent advances machine vision various DL-based algorithms authentication, DL lightweight DL, used such adulteration identification, variety freshness detection, identification by combining them with system or smartphones portable devices. review explores limitations challenges include overfitting, interpretability, accessibility, data privacy, algorithmic bias, design deployment DLs, miniaturization sensing Finally, future developments field are discussed, development real-time detection systems that incorporate combination expansion databases. Overall, techniques expected enable faster, more affordable, accurate authentication methods.

Language: Английский

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Leveraging Convolutional Neural Networks for Disease Detection in Vegetables: A Comprehensive Review

Agronomy, Journal Year: 2024, Volume and Issue: 14(10), P. 2231 - 2231

Published: Sept. 27, 2024

Timely and accurate detection of diseases in vegetables is crucial for effective management mitigation strategies before they take a harmful turn. In recent years, convolutional neural networks (CNNs) have emerged as powerful tools automated disease crops due to their ability learn intricate patterns from large-scale image datasets make predictions samples that are given. The use CNN algorithms important vegetable like potatoes, tomatoes, peppers, cucumbers, bitter gourd, carrot, cabbage, cauliflower critically examined this review paper. This examines the most state-of-the-art techniques, datasets, difficulties related these crops’ CNN-based systems. Firstly, we present summary architecture its applicability classify tasks based on images. Subsequently, explore applications identification crops, emphasizing relevant research, performance measures. Also, benefits drawbacks methods, covering problems with computational complexity, model generalization, dataset size, discussed. concludes by highlighting revolutionary potential transforming crop diagnosis strategies. Finally, study provides insights into current limitations regarding usage computer field detection.

Language: Английский

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A Novel Standalone Internet of Things Enabled Device for Detection of Rotten Fruits in Cold Storage Systems

Published: Sept. 18, 2024

Language: Английский

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