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Investigating the Use of Machine Learning for South African Edible Garnish Yield Prediction

机译:调查机器学习对南非食用装饰产量预测的使用

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This paper focuses on the specific scenario of capturing data in the South African agricultural industry; an industry where it can be difficult, expensive and time consuming to gather information, yet the need for information is critical. The aim is to conduct an introductory study into determining which aspects: location, irrigation, fertilizer application, temperature, or type of growing medium, has the most significant impact on the yield of edible garnish and then to predict the yield of a specific plant. A dataset collected over a three year period and supplemented with empirical knowledge and expert opinion, is analysed and a number of classifiers are applied to select the best strategy for predicting future yield of edible garnish. A random forest classifier showed the most promise and location on the farm was shown to have the largest influence on yield.
机译:本文重点介绍南非农业产业捕获数据的具体情景; 收集信息的困难,昂贵且耗时可能是一个行业,但信息的需求至关重要。 目的是进行介绍性研究,以确定培养培养基的位置,灌溉,肥料应用,温度或类型,对食用厂的产量产生最大的影响,然后预测特定植物的产量。 分析了一个三年内收集的数据集,并分析了经验知识和专家意见,并应用了许多分类机来选择预测食用装饰的未来产量的最佳策略。 随机森林分类器表明,农场上的最理想和位置被证明对产量最大。

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