Leaf rolling detection in maize under complex environments using an improved deep learning method.

IF 3.9 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Yuanhao Wang, Xuebin Jing, Yonggang Gao, Xiaohong Han, Cheng Zhao, Weihua Pan
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Abstract

Leaf rolling is a common adaptive response that plants have evolved to counteract the detrimental effects of various environmental stresses. Gaining insight into the mechanisms underlying leaf rolling alterations presents researchers with a unique opportunity to enhance stress tolerance in crops exhibiting leaf rolling, such as maize. In order to achieve a more profound understanding of leaf rolling, it is imperative to ascertain the occurrence and extent of this phenotype. While traditional manual leaf rolling detection is slow and laborious, research into high-throughput methods for detecting leaf rolling within our investigation scope remains limited. In this study, we present an approach for detecting leaf rolling in maize using the YOLOv8 model. Our method, LRD-YOLO, integrates two significant improvements: a Convolutional Block Attention Module to augment feature extraction capabilities, and a Deformable ConvNets v2 to enhance adaptability to changes in target shape and scale. Through experiments on a dataset encompassing severe occlusion, variations in leaf scale and shape, and complex background scenarios, our approach achieves an impressive mean average precision of 81.6%, surpassing current state-of-the-art methods. Furthermore, the LRD-YOLO model demands only 8.0 G floating point operations and the parameters of 3.48 M. We have proposed an innovative method for leaf rolling detection in maize, and experimental outcomes showcase the efficacy of LRD-YOLO in precisely detecting leaf rolling in complex scenarios while maintaining real-time inference speed.

Abstract Image

利用改进的深度学习方法检测复杂环境下的玉米叶片卷动。
卷叶是植物进化出的一种常见的适应性反应,用于抵御各种环境胁迫的有害影响。深入了解卷叶变化的内在机制为研究人员提供了一个独特的机会,以提高玉米等表现出卷叶现象的作物的抗逆性。为了更深入地了解卷叶现象,必须确定这种表型的发生和程度。传统的人工卷叶检测既慢又费力,而在我们的调查范围内,对高通量卷叶检测方法的研究仍然有限。在本研究中,我们提出了一种利用 YOLOv8 模型检测玉米卷叶的方法。我们的方法(LRD-YOLO)集成了两项重大改进:卷积块注意力模块(Convolutional Block Attention Module)可增强特征提取能力;可变形 ConvNets v2 可增强对目标形状和尺度变化的适应性。通过对包含严重遮挡、叶片尺度和形状变化以及复杂背景情况的数据集进行实验,我们的方法达到了令人印象深刻的 81.6% 的平均精度,超过了目前最先进的方法。此外,LRD-YOLO 模型只需要 8.0 G 浮点运算和 3.48 M 的参数。我们提出了一种创新的玉米卷叶检测方法,实验结果展示了 LRD-YOLO 在复杂场景中精确检测卷叶的功效,同时保持了实时推理速度。
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来源期刊
Plant Molecular Biology
Plant Molecular Biology 生物-生化与分子生物学
自引率
2.00%
发文量
95
审稿时长
1.4 months
期刊介绍: Plant Molecular Biology is an international journal dedicated to rapid publication of original research articles in all areas of plant biology.The Editorial Board welcomes full-length manuscripts that address important biological problems of broad interest, including research in comparative genomics, functional genomics, proteomics, bioinformatics, computational biology, biochemical and regulatory networks, and biotechnology. Because space in the journal is limited, however, preference is given to publication of results that provide significant new insights into biological problems and that advance the understanding of structure, function, mechanisms, or regulation. Authors must ensure that results are of high quality and that manuscripts are written for a broad plant science audience.
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