The Production and prediction of major chinese agricultural fruits using an econometric analysis and machine learning technique

Rehman, Abdul; Zhang, Deyuan; Jingdong, Luan

doi:10.5897/ajar2018.13491

Cited by 2 publications

(3 citation statements)

References 43 publications

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“…We also performed a comparative analysis of the proposed approach with an existing state-of-the-art approach, and the results are presented in Table 3. In the existing appraoch [53], the authors considered the five different categories of fruits: apples, bananas, grapes, citrus, and pears. The main objective of their study was to find a correlation between AGDP (agriculture gross domestic product) and fruit growth.…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

“…Therefore, it is an important research direction to utilize latest machine learning techniques for this analysis. In Table 2, the results of our proposed approach have been compared with an existing approach [53]. In this approach [53], authors have also considered the five different categories of fruit, i.e., apple, banana, grapes, citrus, and pears.…”

Section: Comparison Of Studymentioning

confidence: 99%

“…In Table 2, the results of our proposed approach have been compared with an existing approach [53]. In this approach [53], authors have also considered the five different categories of fruit, i.e., apple, banana, grapes, citrus, and pears. The main objective of their study was to find a correlation between AGDP (agriculture gross domestic product) and fruit growth.…”

Section: Comparison Of Studymentioning

confidence: 99%

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Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

et al. 2021

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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.

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Section: Discussion and Recommendationsmentioning

confidence: 99%

Section: Comparison Of Studymentioning

confidence: 99%

Section: Comparison Of Studymentioning

confidence: 99%

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Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

et al. 2021

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Dynamic perspectives into tropical fruit production: a review of modeling techniques

Mancero-Castillo,

Garcia,

Aguirre-Munizaga

et al. 2024

Front. Agron.

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Modeling the intricate interactions between fruit trees, their environments, soils, and economic factors continues to be a significant challenge in agricultural research globally, requiring a multidisciplinary approach. Despite advances in agricultural technology and algorithms, significant knowledge gaps persist in understanding and modeling these interactions. This review explores basic concepts related to modeling for tropical fruit production. It explains modeling development from sensor technologies, image analysis, databases, and algorithms for decision support systems while considering climate changes or edaphoclimatic limitations. We report the current fruit modeling tendencies showing a significant increase in publications on these topics starting in 2021, driven by the need for sustainable solutions and access to large agricultural databases. This study emphasizes inherent challenges in tropical fruit modeling, such as fruit tree cycles, costly and time-consuming experimentation, and the lack of standardized data. These limitations are evident in tropical fruit, where few models have been reported or validated for cocoa, avocado, durian, dragonfruit, banana, mango, or passion fruit. This study analyzes the classification of the algorithms related to tropical fruit into three main categories: supervised, unsupervised, and reinforcement learning, each with specific applications in agricultural management optimization. Crop classification and yield prediction use supervised models like neural networks and decision trees. Unsupervised models, like K-Means clustering, allow pattern identification without prior labels, which is useful for area segmentation and pest detection. Automation of irrigation and fertilization systems employs reinforcement learning algorithms to maximize efficiency. This multidisciplinary review discusses recent approaches to 1) Modeling Soil health and plant-soil interaction, 2) Yield prediction in tropical fruit orchards, 3) Integrating meteorological models for enhanced tropical fruit production, and 4) Economics of tropical fruit business through modeling. Furthermore, this review illustrates the complexity and multidisciplinary research on models for tropical fruit and platforms using agricultural models. Further opportunities to advance fruit modeling frameworks are indicated, requiring technical knowledge about the fruit crop requirements with user-friendly platforms to collect and access fruit tree data and site-specific agroecological conditions.

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The Production and prediction of major chinese agricultural fruits using an econometric analysis and machine learning technique

Cited by 2 publications

References 43 publications

Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

Dynamic perspectives into tropical fruit production: a review of modeling techniques

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