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Current and future applications of statistical machine learning algorithms for agricultural machine vision systems

机译：农业机器视觉系统统计机器学习算法的当前和未来应用

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摘要

With being rapid increasing population in worldwide, the need for satisfactory level of crop production with decreased amount of agricultural lands. Machine vision would ensure the increase of crop production by using an automated, non-destructive and cost-effective technique. In last few years, remarkable results have been achieved in different sectors of agriculture. These achievements are integrated with machine learning techniques on machine vision approach that cope with colour, shape, texture and spectral analysis from the image of objects. Despite having many applications of different machine learning techniques, this review only described the statistical machine learning technologies with machine vision systems in agriculture due to broad area of machine learning applications. Two types of statistical machine learning techniques such as supervised and unsupervised learning have been utilized for agriculture. This paper comprehensively surveyed current application of statistical machine learning techniques in machine vision systems, analyses each technique potential for specific application and represents an overview of instructive examples in different agricultural areas. Suggestions of specific statistical machine learning technique for specific purpose and limitations of each technique are also given. Future trends of statistical machine learning technology applications are discussed.
机译：随着全球人口迅速增加，需要降低农业土地的令人满意的作物生产水平。机器视觉将通过使用自动化，非破坏性和经济高效的技术来确保作物生产的增加。在过去的几年里，在农业不同部门取得了显着的结果。这些成就与机器视觉方法的机器学习技术集成，该技术应对来自物体图像的颜色，形状，纹理和光谱分析。尽管具有许多不同机器学习技术的应用，但该综述仅描述了由于机器学习应用的广泛区域，仅描述了农业机器视觉系统的统计机器学习技术。两种类型的统计机器学习技术，如监督和无监督的学习，已被用于农业。本文全面调查了电机视觉系统中统计机器学习技术的目前的应用，分析了各种技术潜力，并表示不同农业领域的指导例子的概述。还给出了针对每个技术的特定目的和限制的特定统计机器学习技术的建议。讨论了统计机器学习技术应用的未来趋势。

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