125万参数小模型实现99.5%植物病害识别准确率，适合在普通电脑上离线使用。
A Compact and Efficient 1.251 Million Parameter Machine Learning CNN Model PD36-C for Plant Disease Detection: A Case Study
- 设计轻量级CNN架构，仅125万参数，兼顾高精度与边缘设备部署能力。
- 在8.7万张图像、38类植物病害数据集上测试，平均准确率达99.53%。
- 配套桌面应用支持离线推理，适合农业一线快速部署与实地检测。
深度学习显著提升了基于图像的植物病害诊断性能，得益于硬件进步和数据质量提升。本文提出PD36-C，一种紧凑型卷积神经网络（1,250,694参数，4.77 MB），用于植物病害分类。该模型在包含8.7万张图像、38个类别的New Plant Diseases Dataset上使用TensorFlow Keras训练，具备强鲁棒性与边缘部署能力，并配套一个基于Qt for Python的桌面应用，提供直观图形界面和在通用硬件上的离线推理功能。实验显示，第30轮训练时准确率达到0.99697，38类平均测试准确率为0.9953。各类别表现均匀：玉米叶斑病精度约0.9777，召回约0.9634；苹果黑腐病、樱桃白粉病等众多类别均达到完美精度1.00与召回1.00，无误判且覆盖全面。结果表明，通过精心设计的架构与高质量数据集，小型CNN可达到与最新基线相当的精度，同时满足边缘场景需求。研究也指出天气恶劣、图像质量差及多重病症重叠等现实因素会降低性能，需进一步提升模型鲁棒性。总体而言，PD36-C及其应用流程为智慧农业中的AI辅助病害检测提供了实用、高效的现场解决方案。
原文摘要 · Abstract (English)
Deep learning has markedly advanced image based plant disease diagnosis as improved hardware and dataset quality have enabled increasingly accurate neural network models. This paper presents PD36 C, a compact convolutional neural network (1,250,694 parameters and 4.77 MB) for plant disease classification. Trained with TensorFlow Keras on the New Plant Diseases Dataset (87k images, 38 classes), PD36 C is designed for robustness and edge deployability, complemented by a Qt for Python desktop application that offers an intuitive GUI and offline inference on commodity hardware. Across experiments, training accuracy reached 0.99697 by epoch 30, and average test accuracy was 0.9953 across 38 classes. Per class performance is uniformly high; on the lower end, Corn (maize) Cercospora leaf spot achieved precision around 0.9777 and recall around 0.9634, indicating occasional confusion with visually similar categories, while on the upper end numerous classes including Apple Black rot, Cedar apple rust, Blueberry healthy, Cherry Powdery mildew, Cherry healthy, and all four grape categories achieved perfect precision 1.00 and recall of 1.00, indicating no false positives and strong coverage. These results show that with a well curated dataset and careful architectural design, small CNNs can achieve competitive accuracy compared with recent baselines while remaining practical for edge scenarios. We also note typical constraints such as adverse weather, low quality imagery, and leaves exhibiting multiple concurrent diseases that can degrade performance and warrant future work on domain robustness. Overall, PD36 C and its application pipeline contribute a field ready, efficient solution for AI assisted plant disease detection in smart agriculture.
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