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Plant Seeds Growth Prediction on Greenhouse Using Adaptive Neuro Fuzzy Inference System (ANFIS) Method

机译:使用自适应神经模糊推理系统(ANFIS)方法对温室的植物种子生长预测

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摘要

Food security is a problem that every country had, especially for poor and developing countries. To improve the food security one of the solutions that can be applied is to collaborate technology and agriculture such as greenhouse. The technology that is applied to greenhouse can produce plants with good quality. Good quality plant can be predicted with prediction on the plant seeds in order to develop the plants production just as we expected. Prediction on plant seeds is using the adaptive neuro fuzzy inference system (ANFIS) model which is a combination of fuzzy and neural network. ANFIS will process the data with high complexity and it will provide the prediction result with high accuracy. Plant seeds prediction is using 65 data which divided into two data, specifically 50 training data and 15 testing data. The prediction provides accurate result and will generate 14/15 x 100% = 93.3333% precision with Mean Absolute Deviation (MAD) is 64.3391 from 15 prediction data about 4.2893, Mean Absolute Percentage Error (MAPE) is 5.3485 from 15 prediction data about 0.35657, Mean Square Deviation (MSD) is 9.159 from 15 prediction data about 0.6106.
机译:粮食安全是每个国家的一个问题,特别是穷人和发展中国家。为了改善粮食安全,可以应用的解决方案之一是协作技术和农业,如温室。适用于温室的技术可以生产出质量良好的植物。可以预测植物种子预测的优质植物,以便立即开发植物生产。植物种子的预测是使用自适应神经模糊推理系统(ANFIS)模型,其是模糊和神经网络的组合。 ANFIS将处理具有高复杂性的数据,并且它将提供高精度的预测结果。植物种子预测使用65个数据分为两个数据,特别是50个训练数据和15个测试数据。预测提供了准确的结果,并且将产生14/15×100%= 93.3333%的精度,平均绝对偏差(MAD)为64.3391,从15个预测数据约为4.2893,平均绝对百分比误差(MAPE)为5.3485,从15个预测数据约为0.35657,平均方偏差(MSD)为9.159,从15个预测数据约为0.6106。

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