Weeds pose a significant and fundamental challenge in agriculture, competing with crops for vital resources such as water, nutrients, and sunlight. This competition often leads to reduced crop yields and diminished quality of produce. Additionally, weeds can host pests and diseases that further harm crops, increasing the risk of infestation and reducing farm productivity. Accurate weed identification through deep learning offers a solution, enabling farmers to implement site-specific herbicide spraying, thus lowering herbicide usage and minimizing environmental impact. This study introduces a benchmark crop and weed classification dataset and evaluates seven state-of-the-art deep-learning models for weed identification. The dataset was obtained from potato fields in Punjab, India, over two consecutive growth seasons (2022 and 2023). Seven deep learning models, Convolution Neural Network (CNN)-11, CNN-14, Inceptionv3, AlexNet, VGG16, ResNet50, and the YOLOv8 were trained and tested on this dataset for potato and weed classification. Among these models, YOLOv8 emerges as the top performer, achieving flawless accuracy of 100% with 37.5 million parameters. The custom CNN-11 model, despite having the fewest parameters (2.2 million), achieves 52% accuracy, making it suitable for resource-constrained environments. ResNet50, with its residual networks, also demonstrates exceptional performance with 99% accuracy and a moderate number of parameters (23 million), which can be a significant consideration in environments with limited resources or when deploying models on edge devices. These findings guide researchers and practitioners in selecting optimal models to reduce herbicide usage, minimize environmental impact, and enhance precision agriculture practices. Ultimately, this study advances weed management strategies, supporting sustainable crop management and improving agricultural productivity.

中文翻译：

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分析深度卷积神经网络模型在马铃薯田杂草识别中的性能


杂草在农业中构成了重大而根本的挑战，与农作物争夺水、养分和阳光等重要资源。这种竞争通常会导致农作物产量下降和农产品质量下降。此外，杂草会滋生病虫害，进一步危害农作物，增加侵扰风险并降低农场生产力。通过深度学习进行准确的杂草识别提供了一种解决方案，使农民能够实施特定地点的除草剂喷洒，从而减少除草剂的使用并最大限度地减少对环境的影响。本研究介绍了一个基准作物和杂草分类数据集，并评估了七种最先进的深度学习模型用于杂草识别。该数据集是从印度旁遮普邦的马铃薯田连续两个生长季节（2022 年和 2023 年）获得的。卷积神经网络 （CNN）-11、CNN-14、Inceptionv3、AlexNet、VGG16、ResNet50 和 YOLOv8 这七个深度学习模型在这个数据集上进行了马铃薯和杂草分类的训练和测试。在这些模型中，YOLOv8 表现最佳，实现了 100% 的完美精度，具有 3750 万个参数。自定义 CNN-11 模型尽管参数最少 （220 万），但准确率高达 52%，使其适用于资源受限的环境。ResNet50 及其残余网络还表现出卓越的性能，准确率高达 99%，参数数量适中 （2300 万），这在资源有限的环境中或在边缘设备上部署模型时可能是一个重要的考虑因素。这些发现指导研究人员和从业者选择最佳模型，以减少除草剂的使用，最大限度地减少对环境的影响，并加强精准农业实践。 最终，这项研究推进了杂草管理策略，支持可持续的作物管理并提高农业生产力。