Advances in plant nutrition diagnosis based on remote sensing and computer application
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SMART DATA AGGREGATION INSPIRED PARADIGM & APPROACHES IN IOT APPLNS
Advances in plant nutrition diagnosis based on remote sensing and computer application Deyu Feng1 • Weihong Xu1 • Zhangmi He1 • Wanyi Zhao1 • Mei Yang1 Received: 17 September 2018 / Accepted: 3 December 2018 Ó Springer-Verlag London Ltd., part of Springer Nature 2019
Abstract Hyperspectral remote sensing, visible light remote sensing and canopy color analysis have been widely concerned for rapid diagnosis of crop growth and nutrition. They are expected to develop into potential nondestructive diagnostic techniques for crop nitrogen nutrition in the new era on account of the advantages of stable, rapid, convenient and nondestructive results, together with the good correlation between canopy color parameter NRI and plant nitrogen nutrition index and yield satisfying the demand for nondestructive diagnosis of nitrogen nutrition, and their feasibility to monitor plant growth status and nitrogen nutrition level in real time and quickly. At present, with the rapid development of remote sensing satellite, unmanned aerial vehicles remote sensing and Internet of things, remote sensing will be more and more widely used in plant nutrition diagnosis. Keywords Plant nutrition diagnosis Hyperspectral Canopy color Satellite remote sensing UAV
1 Introduction Traditional plant nutrition diagnosis and fertilization recommendation are mainly based on field sampling and laboratory chemical analysis. This method needs a lot of manpower, material and financial resources in sample collection, testing and data processing. It is not suitable for popularization and application. A series of changes in leaf color, thickness, moisture content and morphological structure are caused by vegetation deficiency, which results in changes in spectral reflectance characteristics. Therefore, remote sensing based on spectral reflectance features of objects to identify objects has become a possible means for real-time monitoring and rapid diagnosis of plant nutrient status. Thomas et al. [1] studied the spectral characteristics of leaves of seven plants under different nitrogen nutrient levels and found that the reflectance of light wave region was increased in all plants under nitrogen & Weihong Xu [email protected] 1
College of Resources and Environmental Sciences, Southwest University, Chongqing 400715, People’s Republic of China
deficiency; however, the increase in degree of reflectivity was different among different plants. The spectral characteristics of rice leaves under nitrogen deficiency and normal nutrition were significantly different, and chlorophyll was considered as the main internal factor leading to the difference of spectral characteristics [2]. In recent years, with the rapid development of remote sensing satellite, UAV remote sensing and Internet of things, remote sensing has been widely used in plant nutrition diagnosis [3, 4]. Compared with other diagnostic methods, hyperspectral remote sensing, visible light re
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