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Detection of spikes is the first important step toward image-based quantitative assessment of crop yield. However, spikes of grain plants occupy only a tiny fraction of the image area and often emerge in the middle of the mass of plant leaves that exhibit similar colors to spike regions. Consequently, accurate detection of grain spikes renders, in general, a non-trivial task even for advanced, state-of-the-art deep neural networks (DNNs). To improve pattern detection in spikes, we propose architectural changes to Faster-RCNN (FRCNN) by reducing feature extraction layers and introducing a global attention module. The performance of our extended FRCNN-A vs. conventional FRCNN was compared on images of different European wheat cultivars, including "difficult" bushy phenotypes from 2 different phenotyping facilities and optical setups. Our experimental results show that introduced architectural adaptations in FRCNN-A helped to improve spike detection accuracy in inner regions. The mean average precision (mAP) of FRCNN and FRCNN-A on inner spikes is 76.0% and 81.0%, respectively, while on the state-of-the-art detection DNNs, Swin Transformer mAP is 83.0%. As a lightweight network, FRCNN-A is faster than FRCNN and Swin Transformer on both baseline and augmented training datasets. On the FastGAN augmented dataset, FRCNN achieved a mAP of 84.24%, FRCNN-A attained a mAP of 85.0%, and the Swin Transformer achieved a mAP of 89.45%. The increase in mAP of DNNs on the augmented datasets is proportional to the amount of the IPK original and augmented images. Overall, this study indicates a superior performance of attention mechanisms-based deep learning models in detecting small and subtle features of grain spikes., Competing Interests: Competing interests: Authors S.U., K.P., and M.T. are affiliated with the PSI company. The remaining authors declare that they have no competing interests., (Copyright © 2024 Sajid Ullah et al.)

Auxin directs plant ontogenesis via differential accumulation within tissues depending largely on the activity of PIN proteins that mediate auxin efflux from cells and its directional cell-to-cell transport. Regardless of the developmental importance of PINs, the structure of these transporters is poorly characterized. Here, we present experimental data concerning protein topology of plasma membrane-localized PINs. Utilizing approaches based on pH-dependent quenching of fluorescent reporters combined with immunolocalization techniques, we mapped the membrane topology of PINs and further cross-validated our results using available topology modeling software. We delineated the topology of PIN1 with two transmembrane (TM) bundles of five α-helices linked by a large intracellular loop and a C-terminus positioned outside the cytoplasm. Using constraints derived from our experimental data, we also provide an updated position of helical regions generating a verisimilitude model of PIN1. Since the canonical long PINs show a high degree of conservation in TM domains and auxin transport capacity has been demonstrated for Arabidopsis representatives of this group, this empirically enhanced topological model of PIN1 will be an important starting point for further studies on PIN structure-function relationships. In addition, we have established protocols that can be used to probe the topology of other plasma membrane proteins in plants., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Auxin and cytokinins have been identified as key regulators of plant development. Recently, these phytohormones have been shown to interact during important developmental processes, including positioning, identity acquisition and maintenance of meristem organizing centres, regulation of balance between cell division and differentiation, and postembryonic de novo organogenesis. Here, we discuss recent advances in our understanding of the underlying molecular mechanisms at the levels of regulating metabolism, signalling, gene expression and protein stability.