Authors

Abstract

The phenotypic information of lettuce leaves can well reflect its health. In order to diagnose the nutrient deficiency types of hydroponic lettuce accurately, non-destructively and quickly in the mid-growth stage, a method for diagnosis of whole lettuce based on random forest algorithm (RF) was proposed. The images of lettuce under four different conditions, K-deficiency, Ca-deficiency, N-deficiency and Normal, were collected and segmented by Extra-green algorithm. Then, features of color, texture and shape were extracted. A RF classification model for the hydroponic lettuce nutrient deficiency diagnosis was constructed and compared with support vector machine (SVM) and back propagation neural network (BP). RF had the best classification effect among the three methods. The overall classification accuracy was 86.32%, Kappa coefficient was 0.82, and it can provide a basis for the prevention and remedies of lettuce deficiency and the scientific management of nutrient solutions.

Abstract in Chinese

生菜叶片的表型信息可以很好地反映其健康状况。为了准确、无损、快速地诊断水培生菜生长中期的缺素类型，以整株水培生菜叶片图像为研究对象，提出一种基于随机森林算法(RF)的缺素诊断方法。采集缺钾、缺钙、缺氮以及正常4种生长条件下的生长中期的水培生菜缺素图像，利用超绿算法分割得到整株水培生菜叶片图像，并提取其颜色、纹理和形状的特征。基于RF建立水培生菜缺素诊断模型，并与支持向量机(SVM)和BP神经网络(BP)进行对比试验。三种方法中RF的分类效果最好,总体分类准确率为86.32%，Kappa系数为0.82，可为水培生菜缺素症防治及采取补救措施以及营养液的科学管理提供依据。