A novel technology to monitor effects of ethylene on the food products’ supply chain: a deep learning approach-Reference-Cited by-同舟云学术

A novel technology to monitor effects of ethylene on the food products’ supply chain: a deep learning approach

Published:2023-12-27 Issue: Volume: Page:
ISSN:1735-1472
Container-title:International Journal of Environmental Science and Technology
language:en
Short-container-title:Int. J. Environ. Sci. Technol.

Author:

Amani M. A.,Aghamohammadi N.^ORCID

Publisher

Springer Science and Business Media LLC

Subject

General Agricultural and Biological Sciences,Environmental Chemistry,Environmental Engineering

Link

https://link.springer.com/content/pdf/10.1007/s13762-023-05328-3.pdf

Reference50 articles.

1. Acosta A, Ochoa A, Rodriguez-Eparza E, et al. (2022) Classification system to detect diseases in apples by using a convolutional neural network. Technological and Industrial Applications Associated With Industry 40. Springer, pp.331–340.

2. Adhitya Y, Prakosa SW, Köppen M et al (2020) Feature extraction for cocoa bean digital image classification prediction for smart farming application. Agronomy 10(11):1642

3. Ahmadi A, Cherifi C, Cheutet V, et al. (2020) Recent advancements in smart manufacturing technology for modern industrial revolution: a survey. J Eng Inf Sci Stud

4. Al Haque AF, Hakim MA, Hafiz R (2021) CNN based automatic computer vision system for strain detection and quality identification of Banana. In: 2021 international conference on automation, control and mechatronics for industry 4.0 (ACMI). IEEE, pp 1–6

5. Amani MA, Marinello F (2022) A deep learning-based model to reduce costs and increase productivity in the case of small datasets: A case study in cotton cultivation. Agriculture 12(2):267

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