测试卷积神经网络在芒果叶病害诊断中的抗干扰能力，发现轻量模型更适配真实场景。
From Code to Field: Evaluating the Robustness of Convolutional Neural Networks for Disease Diagnosis in Mango Leaves
- 构建带19类人工损坏的芒果叶数据集，评估模型在噪声、模糊等条件下的表现
- 轻量模型LCNN在多种真实损坏下表现最佳，平均损伤误差最低
- 提醒农业AI开发需重视鲁棒性，尤其在边缘设备和资源受限地区
通过鲁棒性评估验证人工智能模型的可靠性，是保障智能系统应对现实挑战（如图像噪声、模糊、天气变化）的关键。尽管芒果（Mangifera indica L.）具有全球重要性，但针对其叶片病害诊断模型的鲁棒性研究仍匮乏。本文提出一种评估卷积神经网络（CNN）在恶劣条件下的方法。我们改造了MangoLeafDB数据集，生成包含19种人工损坏、五级严重程度的MangoLeafDB-C。对五种架构（ResNet-50、ResNet-101、VGG-16、Xception、LCNN）进行基准测试，使用F1分数、腐蚀误差（CE）及相对均值腐蚀误差（相对mCE）作为指标。结果表明，轻量级模型LCNN在真实场景中常见的失焦模糊、运动模糊等损坏下优于复杂模型，且达到最低的mCE。现代架构（如ResNet-101）虽在理想条件下准确率高，但在损坏场景下性能显著下降。这提示轻量且专精的模型更适合边缘设备上的实际应用，其中鲁棒性和效率至关重要。研究强调，农业智能系统开发中应纳入鲁棒性评估，尤其在技术条件有限的地区。
原文摘要 · Abstract (English)
The validation and verification of artificial intelligence (AI) models through robustness assessment are essential to guarantee the reliable performance of intelligent systems facing real-world challenges, such as image corruptions including noise, blurring, and weather variations. Despite the global importance of mango (Mangifera indica L.), there is a lack of studies on the robustness of models for the diagnosis of disease in its leaves. This paper proposes a methodology to evaluate convolutional neural networks (CNNs) under adverse conditions. We adapted the MangoLeafDB dataset, generating MangoLeafDB-C with 19 types of artificial corruptions at five severity levels. We conducted a benchmark comparing five architectures: ResNet-50, ResNet-101, VGG-16, Xception, and LCNN (the latter being a lightweight architecture designed specifically for mango leaf diagnosis). The metrics include the F1 score, the corruption error (CE) and the relative mean corruption error (relative mCE). The results show that LCNN outperformed complex models in corruptions that can be present in real-world scenarios such as Defocus Blur, Motion Blur, while also achieving the lowest mCE. Modern architectures (e.g., ResNet-101) exhibited significant performance degradation in corrupted scenarios, despite their high accuracy under ideal conditions. These findings suggest that lightweight and specialized models may be more suitable for real-world applications in edge devices, where robustness and efficiency are critical. The study highlights the need to incorporate robustness assessments in the development of intelligent systems for agriculture, particularly in regions with technological limitations.
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