High-throughput phenotyping as an auxiliary tool in the selection of corn hybrids for agronomic traits

Alcântara, Júlia Ferreira de; Santos, Regimar Garcia dos; Baio, Fábio Henrique Rojo; Júnior, Carlos Antonio da Silva; Teodoro, Paulo Eduardo; Teodoro, Larissa Pereira Ribeiro

doi:10.1590/0034-737x202370010012

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Agronomic trait analysis can be based on the morphological characteristics of agricultural products to obtain whether there is an advantage in the growth of the species, in the same planting area, with growth advantage is an important basis for the selection of good seeds. 8 Based on this idea, this study applied agronomic traits to the comprehensive evaluation of A. tsao-ko at different stages of maturity, which can be judged from the dominance of external morphological characteristics. Based on the principle of genotype similarity, factor analysis is often used to comprehensively evaluate the relationship between agronomic traits, and can test and correlate subtle trait information.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Chemical Changes during Maturation of Amomum tsao-ko Based on GC-MS, FT-NIR, and FT-MIR

He,

Yang,

Wang

2024

ACS Omega

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Amomum tsao-ko Crevost et Lemaire (A. tsao-ko) is widely grown for its high nutritional and economic value. However, the lack of a scientific harvesting and quality control system has resulted in an uneven product quality. The present study was based on A. tsao-ko from four maturity stages from the same growing area, and its chemical trends and quality were evaluated using a combination of agronomic trait analysis, spectroscopy, chromatography, chemometrics, and network pharmacology. The results showed that A. tsao-ko was phenotypically dominant in October. Spectroscopy showed that the absorbance intensity at different maturity stages showed a trend of October > September > August > July. Further chemical differences between A. tsao-ko at different stages of maturity were found by chromatography to originate mainly from alcohol, aromatic, acids, esters, hydrocarbons, ketone, heterocyclic, and aldehydes. The network pharmacology results showed that the active ingredient for the treatment of obesity was present in A. tsao-ko and had high levels in A. tsao-ko in September and October. The results of this study provide a new idea for the comprehensive evaluation of A. tsao-ko and a theoretical basis for the harvesting and resource utilization of A. tsao-ko.

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Section: Introductionmentioning

confidence: 99%

Analysis of Chemical Changes during Maturation of Amomum tsao-ko Based on GC-MS, FT-NIR, and FT-MIR

He,

Yang,

Wang

2024

ACS Omega

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“…Recent advancements in machine learning (ML) and deep learning (DL) have enabled the development of innovative plant phenotyping techniques that can quickly and accurately analyze large amounts of data on plant growth and development [2]. Several studies have been carried out on the automated phenotyping of plants and its genomic analysis [3][4][5], while there is still a lack of research focusing on environmental sustainability and soil degradation due to the overuse of inorganic fertilizers. To address this issue, in this work, a study on an Amaranthus crop is conducted by analyzing phenotypic growth differences in different soil conditions (varying fertilizer dosage) using DL classification techniques.…”

Section: Introductionmentioning

confidence: 99%

M2F-Net: A Deep Learning-Based Multimodal Classification with High-Throughput Phenotyping for Identification of Overabundance of Fertilizers

Dhakshayani¹,

Surendiran²

2023

Agriculture

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Amaranth, a pseudocereal crop which is rich in nutrients and climate resistant, can provide an opportunity to increase food security and nutritional content for the growing population. Farmers rely mainly on synthetic fertilizers to improve the quality and yield of the crop; however, this overuse harms the ecosystem. Understanding the mechanism causing this environmental deterioration is crucial for crop production and ecological sustainability. In recent years, high-throughput phenotyping using Artificial Intelligence (AI) has been thriving and can provide an effective solution for the identification of fertilizer overuse. Influenced by the strength of deep learning paradigms and IoT sensors, a novel multimodal fusion network (M2F-Net) is proposed for high-throughput phenotyping to diagnose overabundance of fertilizers. In this paper, we developed and analyzed three strategies that fuse agrometeorological and image data by assessing fusion at various stages. Initially two unimodal baseline networks were trained: Multi-Layer Perceptron (MLP) on agrometeorological data and a pre-trained Convolutional Neural Network (CNN) model DenseNet-121 on image data. With these baselines, the multimodal fusion network is developed, capable of adeptly learning from image and non-image data and the model’s performance is evaluated in terms of accuracy and Area Under Curve (AUC). Moreover, the fusion approaches that are considered outperformed the unimodal networks remarkably with 91% accuracy. From the experimental result, it is proven that incorporating agrometeorological information and images can substantially boost the classification performance for the overabundance of fertilizer.

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Plant breeding for harmony between sustainable agriculture, the environment, and global food security: an era of genomics‐assisted breeding

Hafeez,

Ali,

Javed

et al. 2023

Planta

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High-throughput phenotyping as an auxiliary tool in the selection of corn hybrids for agronomic traits

Cited by 4 publications

References 21 publications

Analysis of Chemical Changes during Maturation of Amomum tsao-ko Based on GC-MS, FT-NIR, and FT-MIR

Analysis of Chemical Changes during Maturation of Amomum tsao-ko Based on GC-MS, FT-NIR, and FT-MIR

M2F-Net: A Deep Learning-Based Multimodal Classification with High-Throughput Phenotyping for Identification of Overabundance of Fertilizers

Plant breeding for harmony between sustainable agriculture, the environment, and global food security: an era of genomics‐assisted breeding

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