Enhancing site-specific weed detection using deep learning transformer architectures

IF 2.5 2区农林科学 Q1 AGRONOMY

Crop Protection Pub Date : 2024-12-12 DOI:10.1016/j.cropro.2024.107075

Francisco Garibaldi-Márquez, Daniel A. Martínez-Barba, Luis E. Montañez-Franco, Gerardo Flores, Luis M. Valentín-Coronado

引用次数: 0

Abstract

Detection of weeds is essential to implement an intelligent weed control system in natural corn fields. Then, to address this issue, the Swin-UNet, Segmenter, and SegFormer deep learning transformer architectures have been implemented and compared. Furthermore, a simple thresholding method has been performed to enhance the segmentation. Moreover, a large pixel-level annotated image dataset acquired under natural field conditions is introduced to train the models. In addition, the well-known Precision, Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and mean Intersection over Union (mIoU) metrics have been used to evaluate the implemented models’ performance. According to the experimental results, the SegFormer architecture was the best model on each of the three proposed weed detection approaches, achieving a macro performance of up to 94.49%, 95.30%, and 91.26% for Precision, DSC, and mIoU, respectively.

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检测杂草对于在天然玉米田中实施智能杂草控制系统至关重要。为解决这一问题，我们采用了 Swin-UNet、Segmenter 和 SegFormer 深度学习变换器架构，并对其进行了比较。此外，还采用了一种简单的阈值处理方法来增强分割效果。此外，还引入了在自然野外条件下获取的大型像素级注释图像数据集来训练模型。此外，还使用了著名的精度、骰子相似系数（DSC）、联合交叉（IoU）和平均联合交叉（mIoU）指标来评估所实现模型的性能。实验结果表明，SegFormer 架构在三种杂草检测方法中都是最佳模型，其精确度、DSC 和 mIoU 的宏观性能分别高达 94.49%、95.30% 和 91.26%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Crop Protection 农林科学-农艺学

CiteScore

6.10

自引率

3.60%

发文量

200

审稿时长

29 days

期刊介绍： The Editors of Crop Protection especially welcome papers describing an interdisciplinary approach showing how different control strategies can be integrated into practical pest management programs, covering high and low input agricultural systems worldwide. Crop Protection particularly emphasizes the practical aspects of control in the field and for protected crops, and includes work which may lead in the near future to more effective control. The journal does not duplicate the many existing excellent biological science journals, which deal mainly with the more fundamental aspects of plant pathology, applied zoology and weed science. Crop Protection covers all practical aspects of pest, disease and weed control, including the following topics: -Abiotic damage- Agronomic control methods- Assessment of pest and disease damage- Molecular methods for the detection and assessment of pests and diseases- Biological control- Biorational pesticides- Control of animal pests of world crops- Control of diseases of crop plants caused by microorganisms- Control of weeds and integrated management- Economic considerations- Effects of plant growth regulators- Environmental benefits of reduced pesticide use- Environmental effects of pesticides- Epidemiology of pests and diseases in relation to control- GM Crops, and genetic engineering applications- Importance and control of postharvest crop losses- Integrated control- Interrelationships and compatibility among different control strategies- Invasive species as they relate to implications for crop protection- Pesticide application methods- Pest management- Phytobiomes for pest and disease control- Resistance management- Sampling and monitoring schemes for diseases, nematodes, pests and weeds.