Smartphone application-based measurements of stem-base width and plant height in rice seedling

Li, Hongwei; Dong, Wenhao; Li, Zehua; Cao, Xiulong; Tan, Suiyan; Qi, Long; Chen, Xueshen; Xiao, Ronghao; Gong, Hao; Wang, Xicheng; Xu, Ma

doi:10.1016/j.compag.2022.107022

Cited by 8 publications

(3 citation statements)

References 43 publications

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“…In addition, 5 plants per plot were selected due to the amount of plant material to be processed, and 59 samples were obtained to determine the biomass using this method. Plant height was also measured as an indicator of biomass [32,33].…”

Section: Experiments Samplingmentioning

confidence: 99%

Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots

Duque

Patino

Colorado

et al. 2023

Sensors

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The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for selecting the best varieties, improving yield predictions, and supporting ecosystem monitoring and conservation efforts. In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. A UAV acquires multispectral reflectance channels across a rice field of subplots containing different genotypes. Based on the ground-truth data, yields are characterized for the 59 plots and correlated with the Vegetation Indices (VIs) calculated from the photogrammetric mapping. The VIs are weighted by the segmentation of the plants from the soil and used as a feature matrix to estimate, via machine learning models, the biomass and nitrogen of the selected rice genotypes. The genotype IR 93346 presented the highest yield with a biomass gain of 10,252.78 kg/ha and an average daily biomass gain above 49.92 g/day. The VIs with the highest correlations with the ground-truth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. The machine learning model that performed best in estimating the variables of the 59 plots was the Gaussian Process Regression (GPR) model with a correlation factor of 0.98 for wet biomass, 0.99 for dry biomass, and 1 for nitrogen. The results presented demonstrate that it is possible to characterize the yields of rice plots containing different genotypes through ground-truth data and VIs.

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Section: Experiments Samplingmentioning

confidence: 99%

Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots

Duque

Patino

Colorado

et al. 2023

Sensors

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“…After afforestation, seedlings with high nitrogen content in their leaves exhibit strong photosynthesis, which is beneficial for carbohydrate synthesis, thereby inducing rooting and promoting stem growth. Carbohydrates are products of photosynthesis, accounting for 75% [7] of the dry weight of seedlings. After the seedlings are raised and before afforestation, their life activities are maintained entirely by stored carbohydrates, and the health status of the seedlings can also be quantitatively reflected by measuring the chlorophyll content of their leaves.…”

Section: Quality Grading Indicators For Seedlingsmentioning

confidence: 99%

Overview of Research on Seedling Quality Grading Based on Artificial Intelligence

Li,

et al. 2023

AETR

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Testing and grading the quality of seedlings is beneficial for selecting advantageous varieties. This article discusses the grading indicators of seedling quality from three aspects: morphology, physiology, and vitality. It summarizes and prospects the research status of intelligent grading methods for seedling quality at home and abroad, aiming to provide reference for exploring new and more comprehensive intelligent grading methods.

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“…Two-dimensional imaging devices have been widely used to measure basic shape characteristics, including plant height, stembase width, leaf length, and leaf width. In a study of the phenotypic parameters of rice seedlings [20][21][22][23], Li et al [24] proposed a measurement method for stem-base width and height based on the 2D images of rice seedlings. The R 2 value of the stem-base width and height were 0.9832 and 0.9796, respectively.…”

Section: Introductionmentioning

confidence: 99%

A Visual Method of Hydroponic Lettuces Height and Leaves Expansion Size Measurement for Intelligent Harvesting

et al. 2023

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Harvesting is an important procedure for hydroponic lettuces in plant factories. At present, hydroponic lettuces are mainly harvested manually, and the key difficulty in mechanical harvesting is reducing the occurrence of leaf injury. Measuring the size of hydroponic lettuces using the image processing method and intelligently adjusting the operating parameters of the harvesting device are the foundation of high-quality harvesting for lettuces. The overlapped leaves of adjacent hydroponic lettuces cause difficulties in measuring lettuce size, especially the leaves expansion size. Therefore, we proposed an image processing method for measuring lettuce height and leaves expansion size according to the upper contour feature of lettuces and an image included three lettuces. Firstly, the upper contours of the lettuces were extracted and segmented via image preprocessing. Secondly, lettuce height was measured according to the maximum ordinate of the contour. Lastly, the lettuce’s upper contour was fitted to a function to measure the leaves expansion size. The measurement results showed that the maximal relative error of the lettuce height measurements was 5.58%, and the average was 2.14%. The effect of the quadratic function in fitting the upper contour was the best compared with the cubic function and sine function. The maximal relative error of the leaves expansion size measurements was 8.59%, and the average was 4.03%. According to the results of the lettuce height and leaves expansion size measurements, the grabbing parameters of each lettuce were intelligently adjusted to verify the harvesting effect. The harvesting success rates of lettuces was above 90%, and the injured leaves areas of the left, middle, and right lettuces in each image were 192.6 mm2, 228.1 mm2, and 205.6 mm2, respectively. This paper provides a reference for the design and improvement of intelligent harvesters for hydroponic lettuces.

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Smartphone application-based measurements of stem-base width and plant height in rice seedling

Cited by 8 publications

References 43 publications

Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots

Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots

Overview of Research on Seedling Quality Grading Based on Artificial Intelligence

A Visual Method of Hydroponic Lettuces Height and Leaves Expansion Size Measurement for Intelligent Harvesting

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