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Abstract During the last decade, the sorghum aphid (Melanaphis sorghi), previously identified as sugarcane aphid (Melanaphis sacchari), became a serious pest of sorghum, spreading to all sorghum‐producing regions in the United States, Mexico, and South America, where crop losses of 50%–100% have been reported. Developing sorghum cultivars with resistance to this insect is the most sustainable strategy for long‐term pest management. To design cultivars with aphid resistance, comprehensively understanding the mechanisms underlying aphid survival, host plant resistance, and aphid–sorghum interactions is critical. In this review, we summarize the comprehensive efforts to characterize the aphid populations as well as their interaction with sorghum plants via hormonal pathways that trigger various genes including leucine rich repeats, WRKY transcription factors, lipoxygenases, calmodulins, and others. We discuss efforts made during the last decade to identify specific genomic regions and candidate genes that confer aphid resistance, as well as describe recent successes and potential challenges in breeding for aphid resistance. Furthermore, we discuss the use of disruptive technologies like high‐throughput phenotyping, artificial intelligence, or machine learning for developing aphid resistant sorghum cultivars. Integration of these new technologies has the potential to accelerate the development and design of novel traits that confer durable aphid resistance in new sorghum cultivars to defend sorghum against new aphid genotype development.
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Machine learning-based prediction of sorghum biomass from UAV multispectral imagery data
Unmanned aerial vehicle (UAV)-based remote sensing applications in plant phenotyping have received attention in modern plant breeding programs that increasingly have the need to automate time-consuming manual measurements of agronomic traits. This paper focuses on the prediction of sorghum biomass using machine learning algorithms such as Linear Regression, KNeighbors Regressor, and the XGBoost Regressor. Results from a field experiment of 344 sorghum genotypes conducted at the Donald Danforth Plant Science Center (Saint Louis, MO, USA) showed accurate prediction models. The K-Neighbors Regression model performed better than the other two models (R2 = 0.65, RMSE = 4968.60 kg/ha). The developed approach in this study could be used as a decision support tool for sorghum biomass phenotyping in breeding programs.
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- Award ID(s):
- 2133355
- PAR ID:
- 10545749
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-2377-1
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Location:
- Shillong, India
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/I3CS58314.2023.10127516
