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Digital transformation in agriculture:powered by Artificial Intelligence and Machine Learning

机译:农业数字转型:由人工智能和机器学习提供动力

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As the population increases and incomes rise, demand for food also increased. In a recent report published by the FAO it is expected that total demand for agricultural products in 2030 will be about 60% higher than today. More than 83% of this additional demand will be in the developing countries, as nearly all population growth will be there. Although their is systemic progress in the potential to increase production, there arc many challenges that needs to be addressed such as availability of suitable arable land and productivity decline, availability of water required for iirigation, assessment of climate change impact on food production, prevention of damage to agriculture crops due to pests, and others. Although systemic progress has taken place to the boost the agricultural development digital transformation with the application of artificial intelligence and machine learning has become an imperative for the sustenance making mam innovations possible in the field of agriculture. This paper highlights the digital transformation trends and various analytics use cases for digital agriculture highlighting the application of Artificial Intelligence/Machine learning on the data collected with Internet of Things (IoT) Platform that can providcsuitablc acceleration to meet the global demand. The paper also highlights the new analvticslifhyclc with the advent of IoT and the need to establish agriculture innovation, and excellence centers across the countiy designed for specific fanner needs.
机译:随着人口的增加,收入增加,对食物的需求也增加了。在最近由粮农组织发布的一份报告中,预计2030年的农产品的总需求比今天约60%。超过83%的额外需求将在发展中国家,因为几乎所有人口增长都会在那里。虽然他们是系统的潜力进展,但需要解决许多挑战,如适当的耕地和生产力下降,所需的水的可用性,对气候变化对粮食生产的影响,粮食生产的影响由于害虫和其他人因农业作物损坏。虽然系统性进展已经促进了促进农业发展数字转型,但随着人工智能的应用,机器学习已经成为农业领域的硕士创新的必要性。本文突出了数字农业数字农业的数字变换趋势和各种分析用例,突出了人工智能/机器学习对用互联网(物联网)平台收集的数据的应用,可以提供能够提供的加速,以满足全球需求。本文还突出了新的Analvticslifhyclc,随着物联网的出现以及在专为特定粉丝所需的Countiy中建立农业创新,卓越中心的需求。

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