This paper presents a novel computational intelligence vision sensing approach to estimate nutrient content in wheat leaves by analyzing color features of the leaves images captured on field with various lighting conditions. We propose the development of deep sparse extreme learning machines (DSELM) fusion and genetic algorithm (GA) to normalize plant images as well as to reduce color variability due to a variation of sunlight intensities. We also apply the DSELM in image segmentation to differentiate wheat leaves from a complex background. In this paper, four moments of color distribution of the leaves images (mean, variance, skewness, and kurtosis) are extracted and utilized as predictors in the nutrient estimation. We combine a number of DSELMs with committee machine and optimize them using the GA to estimate nitrogen content in wheat leaves. The results have shown the superiority of the proposed method in the term of quality and processing speed in all steps, i.e., color normalization, image segmentation, and nutrient prediction, as compared with other existing methods.

中文翻译：

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用于农业自动化中营养成分估计的计算深度智能视觉传感


本文提出了一种新颖的计算智能视觉传感方法，通过分析在各种照明条件下在田间捕获的叶子图像的颜色特征来估计小麦叶子的营养成分。我们建议开发深度稀疏极限学习机（DSELM）融合和遗传算法（GA）来标准化植物图像并减少由于阳光强度变化而导致的颜色变化。我们还将 DSELM 应用于图像分割，以将小麦叶子与复杂背景区分开来。在本文中，提取了叶子图像颜色分布的四个矩（均值、方差、偏度和峰度）并用作营养估计中的预测因子。我们将许多 DSELM 与委员会机器结合起来，并使用 GA 对其进行优化，以估计小麦叶片中的氮含量。结果表明，与其他现有方法相比，该方法在所有步骤（即颜色归一化、图像分割和营养预测）的质量和处理速度方面均具有优越性。