Applications of AI and IoT Technology in Protected Cultivation for Enhancing Agricultural Productivity: A Concise Review

Yadav, Anamika; Devi, Kshetrimayum Monika; Panme, Florence A.; Kumar, Jitendra

doi:10.1007/978-981-99-4677-8_3

Cited by 1 publication

(2 citation statements)

References 41 publications

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“…Research on AI technology in the agricultural sector highlights substantial global contributions, concentrating on the development of AI algorithms and models to address challenges spanning yield prediction [16], soil fertility analysis [17], water resource management [18], pest control [19], and crop disease detection [20].…”

Section: Research and Application Of Ai Technology In Cultivated Land...mentioning

confidence: 99%

“…First, the practical application of AI-driven analytical methodologies in cultivated land protection underscores the utilization of technologies such as machine learning, big data analytics, and remote sensing [21]. These advancements enable the collection and analysis of extensive agricultural data, facilitating more precise decision making in land management practices [20]. Second, the utilization of AI for yield prediction is explored [19].…”

Section: Research and Application Of Ai Technology In Cultivated Land...mentioning

confidence: 99%

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Artificial-Intelligence-Enhanced Study on the Optimization of the Responsibility and Compensation Mechanism for Provincial Cultivated Land Retention from a Fairness Perspective

Zhu,

2023

Land

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Research Objectives: Considering the complex challenges arising from urbanization, population growth, and the consequential strain on China’s cultivated land resources, we integrate insights from advanced artificial intelligence (AI) technology. The primary objective is to quantitatively assess the cultivated land-holding responsibility of local governments in China from a fairness perspective. Our aim is to integrate the cultivated land responsibility ownership amount into the compensation mechanism for cultivated land protection, thereby promoting fairness, cultivated land conservation, and sustainable agricultural development across Chinese provinces while ensuring efficient agricultural product distribution. Research Methodology: Beyond a traditional literature review and model analysis, we incorporated AI technology for data analysis and model optimization. Findings: ① The findings demonstrate a surplus in both food security and ecological security within cultivated land. ② In-depth analysis via AI revealed that the provinces with the largest surplus include Heilongjiang, Jilin, and Hubei. ③ With the precision provided by AI, we delineated the minimum value of the cultivated land responsibility ownership amount for each province. Eleven provinces met this criterion, primarily located in the northeast and central regions and including Yunnan in the west. ④ From a fairness perspective, coupled with AI insights, we established a compensation mechanism for cultivated land protection that is better suited to the Chinese context. Based on this mechanism, the national average compensation standard was calculated to be 26,900 CNY per hectare. The highest standards were observed in Shanghai, Beijing, and Jiangsu, while the lowest were in Shanxi, Heilongjiang, and Gansu.

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Section: Research and Application Of Ai Technology In Cultivated Land...mentioning

confidence: 99%

Section: Research and Application Of Ai Technology In Cultivated Land...mentioning

confidence: 99%

Artificial-Intelligence-Enhanced Study on the Optimization of the Responsibility and Compensation Mechanism for Provincial Cultivated Land Retention from a Fairness Perspective

Zhu,

2023

Land

View full text Add to dashboard Cite

show abstract

Applications of AI and IoT Technology in Protected Cultivation for Enhancing Agricultural Productivity: A Concise Review

Cited by 1 publication

References 41 publications

Artificial-Intelligence-Enhanced Study on the Optimization of the Responsibility and Compensation Mechanism for Provincial Cultivated Land Retention from a Fairness Perspective

Artificial-Intelligence-Enhanced Study on the Optimization of the Responsibility and Compensation Mechanism for Provincial Cultivated Land Retention from a Fairness Perspective

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