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QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions

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Abstract

Crop population and inter-plant spacing in corn farms can provide useful insight into plant phenotypic analysis and informed establishment decisions, improving crop productivity. Traditionally, farmers have relied on manual inspection to assess crop consistency, such as counting plant stands and manually estimating plant spacing. This assessment is carried out in small predefined areas, leading to insufficient crop consistency analysis on the entire field. Moreover, alternative computer vision techniques computing only one or two key parameters also prove insufficient for accurate crop consistency assessment. This research presents a framework called QuanCro that utilizes red–green–blue images from field machinery to analyze crop parameters such as plant stands counting, plant emergence rate, and plant spacing. It utilizes the state-of-the-art object detection network—You Only Look Once version 7 (YOLOv7), to locate and count corn plants combined with our proposed semantic segmentation model, Small Pyramid-UNet (SP-UNet) architecture, to determine leaf area index. This architecture is designed to be memory efficient and computationally less expensive than similar networks, such as HRNet_Mscale (72.1M) and SegNet (34.65M), as it has approximately 21M parameters. The SP-UNet is further integrated with the Zhang–Suen thinning technique and progressive probabilistic Hough transform for crop row detection and plant spacing information. QuanCro accurately estimates crop densities and identifies inconsistent crop areas. The method is tested using 8000 images and shows a mean average precision of 0.976 for identifying plant stands. The SP-UNet achieves intersection over union scores of 0.973, 0.924, and 0.926 for crops, rows, and backgrounds, respectively.

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Data availability

The data that support the findings of this study are available from Croptimistic Technology Inc. Still, restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of Croptimistic Technology Inc.

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Acknowledgements

Two grants jointly fund the research. The first grant, the Mitacs Accelerate grant (Ref. IT27704), entitled “Estimating Organic Matter, Soil Surface Roughness, Plant Stand Count, and Inter-plant Spacing from High-Resolution RGB Imagery” with Croptimistic Technology Inc. (www.swatmaps.com). The second grant, an NSERC Alliance grant (Ref. ALLRP 549723-19), entitled "Ground Truth Validation of Crop Growth Cycle Using High-Resolution Proximal and Remote Sensing" with CropPro Consulting (www.croppro.ca) and Croptimistic Technology Inc.

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  1. Fatimah Islam, Muhib Ullah, and Abdul Bais have contributed equally to this work.

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  1. Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Pkwy, Regina, SK, S4S 0A2, Canada

    Fatimah Islam, Muhib Ullah & Abdul Bais

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Correspondence to Muhib Ullah.

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Islam, F., Ullah, M. & Bais, A. QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions. Neural Comput & Applic 35, 24877–24896 (2023). https://doi.org/10.1007/s00521-023-08961-8

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