Field estimation of maize plant height at jointing stage using an RGB-D camera

Plant height can be used for assessing plant vigor and predicting biomass and yield. Manual measurement of plant height is time-consuming and labor-intensive. We describe a method for measuring maize plant height using an RGB-D camera that captures a color image and depth information of plants under field conditions. The color image was first processed to locate its central area using the S component in HSV color space and the Density-Based Spatial Clustering of Applications with Noise algorithm. Testing showed that the central areas of plants could be accurately located. The point cloud data were then clustered and the plant was extracted based on the located central area. The point cloud data were further processed to generate skeletons, whose end points were detected and used to extract the highest points of the central leaves. Finally, the height differences between the ground and the highest points of the central leaves were calculated to determine plant heights. The coefficients of determination for plant heights manually measured and estimated by the proposed approach were all greater than 0.95. The method can effectively extract the plant from overlapping leaves and estimate its plant height. The proposed method may facilitate maize height measurement and monitoring under field conditions.

Sensors for measuring plant phenotyping:A review

Food crisis is a matter of prime importance because it becomes more severe as the global population grows.Among the solutions to this crisis,breeding is deemed one of the most effective ways.However,traditional phenotyping in breeding is time consuming and laborious,and the database is insufficient to meet the requirements of plant breeders,which hinders the development of breeding.Accordingly,innovations in phenotyping are urgent to solve this bottleneck.The morphometric and physiological parameters of plant are particularly interested to breeders.Numerous sensors have been employed and novel algorithms have been proposed to collect data on such parameters.This paper presents a brief review on the parameter measurement for phenotyping to describe its development in recent years.Some parameters that have been measured in phenotyping are introduced and discussed,including plant height,leaf parameters,in-plant space,chlorophyll,water stress,and biomass.And the measurement methods of each parameter with different sensors were classified and compared.Some comprehensive measurement platforms were also summarized,which are able to measure several parameters simultaneously.Besides,some deficiencies of phenotyping should be addressed,and novel methods should be proposed to reduce cost,improve efficiency,and promote phenotyping in the future.

Measurement and prediction of tomato canopy apparent photosynthetic rate

Given the lack of technical conditions and research methods,instruments that can measure the canopy apparent photosynthetic rate have low precision and are rarely studied.Comparative studies on canopy apparent photosynthetic rate and single leaf photosynthetic rate are also relatively few.This study aims to measure and predict the canopy apparent photosynthetic rate of tomato.A canopy apparent photosynthetic rate measuring system,which was comprised of a wireless sensor network(WSN),an assimilation chamber,and a LI-6400XT photosynthetic rate instrument was established.The system was used to determine the greenhouse environmental parameters and CO2 absorptive capacity of the whole growth stage of tomato.A semi-closed assimilation chamber was designed as a side opening to conveniently measure the canopy apparent photosynthetic rate.WSN nodes were placed in the chamber to monitor environmental parameters,including air temperature,air humidity,and assimilation chamber temperature.A grid and pixel conversion method was used to measure the whole plant leaf areas of tomato.As a semi-closed measurement system,the assimilation chamber was used to calculate the canopy apparent photosynthetic rate.To conduct a comparative research on the canopy apparent photosynthetic rate and the single leaf photosynthetic rate,the LI-6400XT portable photosynthesis system was used to measure the single leaf photosynthetic rate,and the support vector machine was used to establish the prediction model of canopy apparent photosynthetic rate.The experimental results indicated that the correlation coefficients of the photosynthesis prediction model in the seeding and flowering stages were 0.9420 and 0.9226,respectively,showing the high accuracy of the SVM model.