Rapid Identification of Potassium Nutrition Stress in Rice Based on Machine Vision and Object-Oriented Segmentation. (29th September 2019)

• Record Type: Journal Article
• Authors: Chen, Lisu; Huang, Shihan; Sun, Yuanyuan; Zhu, Enyan; Wang, Ke
• Other Names: Lee Young Jong Academic Editor.
• Is Part Of: Journal of spectroscopy. Volume 2019(2019)
• Publication Date: 2019-09-29
• Subjects: Spectrum analysis -- Periodicals; 543.505
• DOI: 10.1155/2019/4623545
• Languages: English
• ISSNs: 2314-4920

Abstract

Abstract : Special symptoms could be observed on rice leaves when exposed to potassium deficiency, and these symptoms usually display differently under different potassium levels, which offer a foundation for rapid nutrition diagnosis. In this research study, two years of hydroponic experiments on rice (providing 5 levels of potassium nutrition from extremely short to normal) were carried out and the leaf images were acquired by optical scanning at four growth periods. To diagnose the potassium nutrition content, the special symptoms including the yellowish brown leaf margin and the necrotic spots were segmented and quantized by the object-oriented method from leaf images, and the 6 further spectral characteristics of leaf were extracted by the image color analyzing function of MATLAB software. Based on the relationship between potassium content and leaf characteristics, the G value (average value of G channel in the RGB color model) calculated from the entire leaf and leaf tip, the area of yellowish leaf margin, and the number of necrotic spots were applied in the establishment of the identification model of potassium stress by using the support vector machine (SVM). The results indicated that the overall identification accuracies of rice potassium nutrition contents were 90%, 94%, 94%, and 96% at four different growth periods (productive tillering stage, invalid tillering stage, jointing stage, and booting stage), respectively. The data obtained from another year were used to validate the model, and the identification accuracies were 94%, 78%, 80%, and 84%, respectively. Generally speaking, the extraction of the specific symptoms by using object-oriented segmentation is an extension of machine vision technology in diagnosing potassium deficiency, and its application in diagnosing plant nutrition is valuable for the quantization of effective characteristics and improvement of identification accuracy.