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1. Bird-view 3D Reconstruction for Crops with Repeated Textures

   Guoyu Lu (January 2023, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   Large-scale in-situ 3D reconstruction of crop fields presents a challenging task, as the 3D crop structures play a crucial role in plant phenotyping and significantly influence crop growth and yield. While existing efforts focus on close range plants, only a limited number of deep learning-based methods have been developed explicitly for large-scale 3D crop reconstruction, mainly due to the scarcity of large-scale crop sensing data. In this paper, we leverage unmanned aerial vehicles (UAVs) in agriculture and utilize a recently captured multiview real-world snap beans crop dataset to develop an unsupervised structure-from-motion (SfM) framework. Our framework is designed specifically for reconstructing large-scale 3D crop structures. It addresses the challenge of inaccurate depth inference caused by excessively repeated patterns in the crop dataset, resulting in highly accurate 3D crop reconstruction for large-scale scenarios. Through experiments conducted on the crop dataset, we demonstrate the accuracy and robustness of our 3D crop reconstruction algorithm. The application of our proposed framework has the potential to advance research in agriculture, enabling better plant phenotyping and understanding of crop growth and yield. 

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2. Advancing Crop Transformation in the Era of Genome Editing

   https://doi.org/10.1105/tpc.16.00196  

   Altpeter, F.; Springer, N.M.; Bartley, L.E.; Blechl, A.E.; Brutnell, T.P.; Citovsky, V.; Conrad, L.J.; Gelvin, S.B.; Jackson, D.P.; Kausch, A.P.; et al (July 2016, The Plant cell)

   Plant transformation has enabled fundamental insights into plant biology and revolutionized commercial agriculture. Unfortunately, for most crops, transformation and regeneration remain arduous even after more than thirty years of technological advances. Genome editing provides new opportunities to enhance crop productivity, but relies on genetic transformation and plant regeneration, which are bottlenecks in the process. Herein we review the state of plant transformation and point to innovations needed to enable genome editing in crops. Plant tissue culture methods need optimization and simplification for efficiency and minimize time in culture. Currently, specialized facilities exist for crop transformation. Single cell and robotic techniques should be developed for high throughput genomic screens. Utilization of plant genes involved in developmental reprogramming, wound response, and/or homologous recombination could boost recovery of transformed plants. Engineering universal Agrobacterium strains and recruitment of other microbes, such as Ensifer or Rhizobium, could facilitate delivery of DNA and proteins into plant cells. Synthetic biology should be employed for de novo design of transformation systems. Genome editing is a potential game-changer in crop genetics when plant transformation systems are optimized. 

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3. KAT4IA: $K$ -Means Assisted Training for Image Analysis of Field-Grown Plant Phenotypes

   https://doi.org/10.34133/2021/9805489  

   Guo, Xingche; Qiu, Yumou; Nettleton, Dan; Yeh, Cheng-Ting; Zheng, Zihao; Hey, Stefan; Schnable, Patrick S. (August 2021, Plant Phenomics)

   null (Ed.)

   High-throughput phenotyping enables the efficient collection of plant trait data at scale. One example involves using imaging systems over key phases of a crop growing season. Although the resulting images provide rich data for statistical analyses of plant phenotypes, image processing for trait extraction is required as a prerequisite. Current methods for trait extraction are mainly based on supervised learning with human labeled data or semisupervised learning with a mixture of human labeled data and unsupervised data. Unfortunately, preparing a sufficiently large training data is both time and labor-intensive. We describe a self-supervised pipeline (KAT4IA) that uses K -means clustering on greenhouse images to construct training data for extracting and analyzing plant traits from an image-based field phenotyping system. The KAT4IA pipeline includes these main steps: self-supervised training set construction, plant segmentation from images of field-grown plants, automatic separation of target plants, calculation of plant traits, and functional curve fitting of the extracted traits. To deal with the challenge of separating target plants from noisy backgrounds in field images, we describe a novel approach using row-cuts and column-cuts on images segmented by transform domain neural network learning, which utilizes plant pixels identified from greenhouse images to train a segmentation model for field images. This approach is efficient and does not require human intervention. Our results show that KAT4IA is able to accurately extract plant pixels and estimate plant heights. 

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4. Silencing ZmPP2C‐A10 with a foxtail mosaic virus (FoMV) derived vector benefits maize growth and development following water limitation

   https://doi.org/10.1111/plb.13568  

   Nihranz, C. T.; Guzchenko, I. A.; Casteel, C. L. (September 2023, Plant Biology)

   Abstract Global climate change is causing more frequent and severe droughts, which can have negative impacts on plant growth and crop productivity. Under drought conditions, plants produce the hormone ABA (abscisic acid), which regulates adaptive responses, such as stomatal closure and root elongation. Plant viruses have been used in the lab to convey new traits to plants and could also be used to increase production of ABA or to enhance downstream plant drought resistance responses.In this study, foxtail mosaic virus (FoMV) was used to silenceZmPP2C‐A10, a negative regulator of ABA signalling, in maize (Zea maysL.). Both silenced and control plants were exposed to an 8‐day drought treatment, followed by a 30‐day period of rewatering, after which indicators of drought resistance were measured.After drought treatment, we observed a nearly twofold increase in expression of a stress‐mitigation gene,ZmRAB17, reduced chlorophyll fluorescence changes (indicator of stress), and increased plant biomass and development in theZmPP2C‐A10‐silenced maize compared to controls.These results demonstrate that the FoMV system can be used to silence endogenous expression ofZmPP2C‐A10and increase maize tolerance to drought. This could offer a useful tool to improve crop traits and reduce yield loss during the growing season. 

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5. Transdisciplinary Collaborations for Advancing Sustainable and Resilient Agricultural Systems

   https://doi.org/10.1111/gcb.70142  

   Bacheva, Vesna; Madison, Imani; Baldwin, Mathew; Baker, Justin; Beilstein, Mark; Call, Douglas_F; Deaver, Jessica_A; Efimenko, Kirill; Genzer, Jan; Grieger, Khara; et al (April 2025, Global Change Biology)

   ABSTRACT Feeding the growing human population sustainably amidst climate change is one of the most important challenges in the 21st century. Current practices often lead to the overuse of agronomic inputs, such as synthetic fertilizers and water, resulting in environmental contamination and diminishing returns on crop productivity. The complexity of agricultural systems, involving plant‐environment interactions and human management, presents significant scientific and technical challenges for developing sustainable practices. Addressing these challenges necessitates transdisciplinary research, involving intense collaboration among fields such as plant science, engineering, computer science, and social sciences. Five case studies are presented here demonstrating successful transdisciplinary approaches toward more sustainable water and fertilizer use. These case studies span multiple scales. By leveraging whole‐plant signaling, reporter plants can transform our understanding of plant communication and enable efficient application of water and fertilizers. The use of new fertilizer technologies could increase the availability of phosphorus in the soil. To accelerate advancements in breeding new cultivars, robotic technologies for high‐throughput plant screening in different environments at a population scale are discussed. At the ecosystem scale, phosphorus recovery from aquatic systems and methods to minimize phosphorus leaching are described. Finally, as agricultural outputs affect all people, integration of stakeholder perspectives and needs into research is outlined. These case studies highlight how transdisciplinary research and cross‐training among biologists, engineers, and social scientists bring diverse expertise to tackling grand challenges in sustainable agriculture, driving discovery and innovation. 

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