Richismita Hazra, Amity University Kolkata
Tea- one of the most popular beverages, consumed by people worldwide, is produced from the tea plant (Camellia sinensis L.), a leaf harvested crop which is primarily cultivated in Asia for the production of green, oolong and black teas. Tea cultivation process though commercially vibrant suffers a drawback due to its Nitrogen (N2) requirement. Nitrogen being the most demanded element for photosynthetic function and growth in plants improves the yield and quality of the crops (tea). Hence Nitrogen has become the call for the hour in crop production because of its yield-enhancing property.
However, groundwater contamination by nitrate-N (NO3-N) is a serious problem in many countries and Nitrogen fertilizers apart from causing leaching, are a major cause of ozone layer depletion (N2O). Furthermore, the application of excessive Nitrogen- fertilizer elevates management costs even in modern large-scale agricultural production. Tea fields in Japan, tend to receive higher rates of Nitrogen fertilization mostly with ammonium sulphate that even exceeds 1000kg Nitrogen per hectare thereby having an adverse effect on the surrounding environment.
The leaf Nitrogen content and Chlorophyll (Chl) content in plant green leaf is positively correlated because Nitrogen is a structural element of Chlorophyll and it affects the greenness of leaf and Chlorophyll accumulation. This has been reported in numerous plant species, and non-destructive and rapid Nitrogen status estimation has been conducted using Chlorophyll meters and also hyperspectral sensing has been applied for nutritional diagnosis. But decreased Chlorophyll content that is caused due to reasons which are not related to Nitrogen deficiency requires Chlorophyll and Nitrogen content to be decoupled from remote sensing data in order to assess various stress and pathogens. Machine learning algorithms have the ability to autonomously solve large nonlinear problems using datasets from multiple variables and provide an appropriate framework for data-driven decision making as well as for incorporating expert knowledge into the algorithms.
This study aimed at the determination of differences in hyperspectral reflectance for the accurate estimation of Nitrogen and Chlorophyll contents in green leaves (GL) and yellow leaves (YL) with the aid of machine learning algorithms. The results suggested that the un-crewed aerial vehicles (drones) will enable the high throughput estimation of Nitrogen and Chlorophyll statuses in canopy-scale tea gardens. Furthermore, yellow leaves inclusions contributed to the detection of Nitrogen content specific hyperspectral regions that are not dependent on Chlorophyll content in the near-infrared reflectance area. All these techniques could be potentially applied for the improvement of irregularities in fertilizers and real-time diagnostics of physiological changes of status in large farms.
Also read: Is zinc deficiency leading to more COVID deaths?
Source: https://doi.org/10.1038/s41598-020-73745-2
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