Khan, T., Sherazi, Hafiz Husnain Raza ORCID: https://orcid.org/0000-0001-8152-4065, Ali, M. and Butt, U.M. (2021) Deep learning-based growth prediction system: A use case of china agriculture. Agronomy, 11 (8). p. 1551.

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Abstract

Agricultural advancements have significantly impacted people’s lives and their surroundings in recent years. The insufficient knowledge of the whole agricultural production system and conventional ways of irrigation have limited agricultural yields in the past. The remote sensing innovations recently implemented in agriculture have dramatically revolutionized production efficiency by offering unparalleled opportunities for convenient, versatile, and quick collection of land images to collect critical details on the crop’s conditions. These innovations have enabled automated data collection, simulation, and interpretation based on crop analytics facilitated by deep learning techniques. This paper aims to reveal the transformative patterns of old Chinese agrarian development and fruit production by focusing on the major crop production (from 1980 to 2050) taking into account various forms of data from fruit production (e.g., apples, bananas, citrus fruits, pears, and grapes). In this study, we used production data for different fruits grown in China to predict the future production of these fruits. The study employs deep neural networks to project future fruit production based on the statistics issued by China’s National Bureau of Statistics on the total fruit growth output for this period. The proposed method exhibits encouraging results with an accuracy of 95.56% calculating by accuracy formula based on fruit production variation. Authors further provide recommendations on the AGR-DL (agricultural deep learning) method being helpful for developing countries. The results suggest that the agricultural development in China is acceptable but demands more improvement and government needs to prioritize expanding the fruit production by establishing new strategies for cultivators to boost their performance.

Item Type:	Article
Identifier:	10.3390/agronomy11081551
Subjects:	Computing
Depositing User:	Marc Forster
Date Deposited:	11 Nov 2024 14:13
Last Modified:	11 Nov 2024 14:15
URI:	https://repository.uwl.ac.uk/id/eprint/12878

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