In recent years, rice seedling raising factories have gradually been promoted in China. The seedlings bred in the factory need to be selected manually and then transplanted to the field. Growth-related traits such as height and biomass are important indicators for quantifying the growth of rice seedlings. Nowadays, the development of image-based plant phenotyping has received increasing attention, however, there is still room for improvement in plant phenotyping methods to meet the demand for rapid, robust and low-cost extraction of phenotypic measurements from images in environmentally-controlled plant factories. In this study, a method based on convolutional neural networks (CNNs) and digital images was applied to estimate the growth of rice seedlings in a controlled environment. Specifically, an end-to-end framework consisting of hybrid CNNs took color images, scaling factor and image acquisition distance as input and directly predicted the shoot height (SH) and shoot fresh weight (SFW) after image segmentation. The results on the rice seedlings dataset collected by different optical sensors demonstrated that the proposed model outperformed compared random forest (RF) and regression CNN models (RCNN). The model achieved R values of 0.980 and 0.717, and normalized root mean square error (NRMSE) values of 2.64% and 17.23%, respectively. The hybrid CNNs method can learn the relationship between digital images and seedling growth traits, promising to provide a convenient and flexible estimation tool for the non-destructive monitoring of seedling growth in controlled environments.
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http://dx.doi.org/10.3389/fpls.2023.1165552 | DOI Listing |
Front Plant Sci
December 2024
International Magnesium Institute, College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou, China.
In recent years, the global rise in temperatures has led to drought and heat becoming major environmental stresses that limit plant growth. Previous research has demonstrated the potential of in augmenting plant stress resistance. However, specific studies on its effects and underlying mechanisms in cuttings of , and Planch are relatively limited.
View Article and Find Full Text PDFFront Plant Sci
December 2024
National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China.
Introduction: Drought stress severely hampers seedling growth and root architecture, resulting in yield penalties. Seed priming is a promising approach to tolerate drought stress for stand establishment and root development.
Methods: Here, various seed priming treatments, .
Aboveground biomass (AGB) is a key indicator of crop nutrition and growth status. Accurately and timely obtaining biomass information is essential for crop yield prediction in precision management systems. Remote sensing methods play a key role in monitoring crop biomass.
View Article and Find Full Text PDFBMC Plant Biol
December 2024
Biobreeding Institute, Xianghu Laboratory, Hangzhou, 311231, China.
This study delves into the combined effects of seasonal climate variations and MIPS1 gene mutations on the germination rates of soybean cultivars TW-1 and TW75. Through comprehensive metabolomic and transcriptomic analyses, we identified key KEGG pathways significantly affected by these factors, including starch and sucrose metabolism, lipid metabolism, and amino acid biosynthesis. These pathways were notably disrupted during the spring, leading to an imbalance in metabolic reserves critical for seedling development.
View Article and Find Full Text PDFInt J Biol Macromol
December 2024
AI Agri-Tech Research Center, Chonnam National University, Gwangju 61186, Republic of Korea; Department of Convergence Biosystems Engineering, Chonnam National University, Gwangju 61186, Republic of Korea; BK21 Interdisciplinary Program in IT-Bio Convergence System, Chonnam National University, Gwangju 61186, Republic of Korea. Electronic address:
Hydrogels in agriculture offer controlled release, however, face issues with rapid disintegration, swift release, and inability to protect active ingredients. To overcome this, the study presents a hydrogel delivery system that uses dopamine-functionalized nanoporous diatom (DE-PDA) microparticles entrapped in alginate and chitosan matrices to deliver plant growth hormone, gibberellic acid (GA) that suffers from instability, limiting its field application. Developed GA@hydrogel beads exhibited an encapsulation efficiency of 85.
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