Evaluation of Image-Based Phenotyping Methods for Measuring Water Yam (Dioscorea alata L.) Growth and Nitrogen Nutritional Status under Greenhouse and Field Conditions

Abstract

New management practices must be developed to improve yam productivity. By allowing non-destructive analyses of important plant traits, image-based phenotyping techniques could help developing such practices. Our objective was to determine the potential of image-based phenotyping methods to assess traits relevant for tuber yield formation in yam grown in the glasshouse and in the field. We took plant and leaf pictures with consumer cameras. We used the numbers of image pixels to derive the shoot biomass and the total leaf surface and calculated the ‘triangular greenness index’ (TGI) which is an indicator of the leaf chlorophyll content. Under glasshouse conditions, the number of pixels obtained from nadir view (view from the top) was positively correlated to shoot biomass, and total leaf surface, while the TGI was negatively correlated to the SPAD values and nitrogen (N) content of diagnostic leaves. Pictures taken from nadir view in the field showed an increase in soil surface cover and a decrease in TGI with time. TGI was negatively correlated to SPAD values measured on diagnostic leaves but was not correlated to leaf N content. In conclusion, these phenotyping techniques deliver relevant results but need to be further developed and validated for application in yam.

Plant phenotyping relevance

ヤムの成長・窒素栄養状態を画像から推定する手法の評価と妥当性検証が研究の中心であり、植物形質の取得方法を扱っている。

Code and data availability

The paper's phenotype/trait measurements (greenhouse pixel-based biomass/leaf area data, TGI/SPAD/N correlations, field soil cover data) are not deposited in any public repository; the Data Availability Statement says they are available only on request from the first authors. The Supplementary Materials (Figures S1-S4,

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