Plant breeding for increased sustainability: challenges, opportunities and progress

Buerstmayr, Hermann; Dreccer, M. Fernanda; Miladinović, Dragana; Qiu, Lijuan; Rajcan, Istvan; Reif, Jochen C.; Varshney, Rajeev K.; Vollmann, J.

doi:10.1007/s00122-022-04238-1

Cited by 3 publications

(2 citation statements)

References 25 publications

(18 reference statements)

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“…In recent decades, organic farming has become more and more popular due to the enormous negative impact of intensive agriculture on both customers and the environment (Kazimierczak et al, 2020). Furthermore, there is an evident increase in demand for organically produced crops worldwide (Buerstmayr et al, 2022). However, the main concern still centres around the potential of organic farming to produce enough food supplies for the human population—projected to be 10 billion in 2050.…”

Section: Introductionmentioning

confidence: 99%

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Evidence of scientific research on organic plant breeding: A bibliometric study

Mitrović¹,

Zorić²,

Terzić³

et al. 2023

Plant Breeding

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Use of varieties bred under organic conditions is essential in order to minimize the yield gap between organic and conventional agriculture. The aim of this study was to analyse research publications related to the topic ‘organic plant breeding’ from the Web of Science database using bibliometric science mapping and visualization tools. The number of analysed documents in the bibliographic dataset was 204 from the 53 sources. The overall trend in the organic plant breeding literature showed that the number of publications increased during the observed time‐span. We found that in total, 65 countries and 337 institutions are active in the field of organic plant breeding with a high degree of international collaboration. The top five countries according to the number of publications were the United States, Italy, Germany, France, and Canada, while the most active institutions were Wageningen University, Iowa State University, University of Alberta, University of Copenhagen, and University of Hohenheim. All keywords from the organic plant breeding research in the agronomy category were separated into seven clusters for different research topics. Although there is evident progress viewed through the increased trend in the number of publications, organic plant breeding needs further expansion and development. This is especially through the implementation of novel plant breeding techniques and methods aiming to improve traits that are highly specific to organic conditions.

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Section: Introductionmentioning

confidence: 99%

“…Furthermore, there is an evident increase in demand for organically produced crops worldwide (Buerstmayr et al, 2022). However, the main concern still centres around the potential of organic farming to produce enough food supplies for the human population-projected to be 10 billion in 2050.…”

Section: Introductionmentioning

confidence: 99%

Evidence of scientific research on organic plant breeding: A bibliometric study

Mitrović¹,

Zorić²,

Terzić³

et al. 2023

Plant Breeding

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High‐Throughput Robotic Phenotyping for Quantifying Tomato Disease Severity Enabled by Synthetic Data and Domain‐Adaptive Semantic Segmentation

He,

Li,

Zhang

et al. 2024

Journal of Field Robotics

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Plant diseases cause an annual global crop loss of 20%–40%, leading to estimated economic losses of 30–50 billion dollars. Tomatoes are susceptible to more than 200 diseases. Breeding disease‐resistant cultivars is more cost‐effective and environmentally sustainable than the frequent use of pesticides. Traditional breeding methods for disease resistance, relying on direct visual observation to measure disease‐related traits, are time‐consuming, inaccurate, expensive, and require specific knowledge of tomato diseases. High‐throughput disease phenotyping is essential to reduce labor costs, improve measurement accuracy, and expedite the release of new varieties, thereby more effectively identifying disease‐resistant crops. Precision agriculture efforts have primarily focused on detecting diseases on individual tomato leaves under controlled laboratory conditions, neglecting the assessment of disease severity of the entire plant in the field. To address this, we created a synthetic data set using existing field and individual leaf data sets, leveraging a game engine to minimize additional data labeling. Consequently, we developed a customized unsupervised domain‐adaptive tomato disease segmentation algorithm that monitors the entire tomato plant and determines disease severity based on the proportion of affected leaf areas. The system‐derived disease percentages show a high correlation with manually labeled data, evidenced by a correlation coefficient of 0.91. Our research demonstrates the feasibility of using ground robots equipped with deep‐learning algorithms to monitor tomato disease severity under field conditions, potentially accelerating the automation and standardization of whole‐plant disease severity monitoring in tomatoes. This high‐throughput disease phenotyping system can also be adapted to analyze diseases in other crops with similar foliar diseases, such as maize, soybeans, and cotton.

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