利用 KNN-LR 模型进行作物生长预测

Attaluri Harshitha, Beebi Naseeba, Narendra Kumar Rao, Abbaraju Sai Sathwik, Nagendra Panini Challa
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摘要

农业研究正在不断扩大。农业在很大程度上依赖于温度、湿度和降雨量等地球和环境因素来预测作物。作物预测是农业中的一个关键问题,而机器学习是这一领域的新兴研究领域。任何种植者都很想知道自己能预测到多少收成。过去,生产者可以控制种植产品的选择、对其生长过程的监控以及收获的时间。但如今,由于气候的突变,农业界发现要想继续发展具有挑战性。因此,机器学习技术逐渐取代了传统的预测方法。本研究采用了这些技术来确定作物产量。使用有效的特征选择技术将原始数据转化为与机器学习兼容的数据集至关重要,这样才能保证特定的机器学习(ML)模型以高精度运行。减少冗余数据,只使用与确定模型最终输出高度相关的数据特征,将提高模型的准确性。为了保证模型只包含最重要的特征,有必要使用最佳特征选择。如果我们在建立模型的过程中,不先研究原始数据中的每一个特征的功能,就把它们组合在一起,那么我们的模型就会变得过于复杂。此外,机器学习模型的时间和面积复杂度也会随着加入对模型性能影响不大的新特征而增加。研究结果表明,与当前的分类方法相比,集合技术的预测准确率更高。
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Crop Growth Prediction using Ensemble KNN-LR Model
Research in agriculture is expanding. Agriculture in particular relies heavily on earth and environmental factors, such as temperature, humidity, and rainfall, to forecast crops. Crop prediction is a crucial problem in agriculture, and machine learning is an emerging study area in this area. Any grower is curious to know how much of a harvest he can anticipate. In the past, producers had control over the selection of the product to be grown, the monitoring of its development, and the timing of its harvest. Today, however, the agricultural community finds it challenging to carry on because of the sudden shifts in the climate. As a result, machine learning techniques have increasingly replaced traditional prediction methods. These techniques have been employed in this research to determine crop production. It is critical to use effective feature selection techniques to transform the raw data into a dataset that is machine learning compatible in order to guarantee that a particular machine learning (ML) model operates with a high degree of accuracy. The accuracy of the model will increase by reducing redundant data and using only data characteristics that are highly pertinent in determining the model's final output. In order to guarantee that only the most important characteristics are included in the model, it is necessary to use optimal feature selection. Our model will become overly complex if we combine every characteristic from the raw data without first examining their function in the model-building process. Additionally, the time and area complexity of the Machine learning model will grow with the inclusion of new characteristics that have little impact on the model's performance. The findings show that compared to the current classification method, an ensemble technique provides higher prediction accuracy.
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