# Building a Plant Disease Classifier from Scratch: PyTorch CNN Practice and MLOps Best Practices

> A complete deep learning project demonstrating how to build a convolutional neural network from scratch to achieve high-precision classification of 38 types of plant leaf diseases, integrating modern MLOps practices.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-13T13:54:53.000Z
- 最近活动: 2026-05-13T14:03:04.991Z
- 热度: 163.9
- 关键词: 深度学习, 卷积神经网络, PyTorch, 植物病害识别, 计算机视觉, MLOps, 正则化, 学习率调度, 农业AI, 图像分类
- 页面链接: https://www.zingnex.cn/en/forum/thread/pytorch-cnnmlops
- Canonical: https://www.zingnex.cn/forum/thread/pytorch-cnnmlops
- Markdown 来源: floors_fallback

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## Project Introduction: Building a High-Precision Plant Disease Classifier from Scratch and MLOps Practices

This project demonstrates how to build a convolutional neural network (CNN) from scratch to achieve high-precision classification of 38 types of plant leaf diseases, integrating modern MLOps practices. Key highlights include: no reliance on pre-trained models, achieving a test accuracy of 98.72% through regularization and dynamic learning rate scheduling, integrating Weights & Biases for experiment management, and structured code organization, providing a reference for similar projects.

## Project Background: Technical Challenges in Agricultural Disease Identification

Global agriculture faces billions of dollars in losses due to plant diseases. Traditional identification methods relying on expert experience are inefficient and difficult to scale. Deep learning technology provides new ideas for automatic disease identification, but building a practical system requires solving challenges such as overfitting, training efficiency, experimental reproducibility, and post-deployment monitoring. This project offers a complete technical solution.

## Model Construction and Training Strategy

The project designs a CNN architecture for 224x224 RGB images, achieving hierarchical feature extraction by using shallow layers to capture low-level features (edges, textures) and deep layers to combine high-level features (lesion shape, color distribution). During training, L2 regularization (decay coefficient 1e-4) is used to combat overfitting, and ReduceLROnPlateau is employed to dynamically adjust the learning rate (large initial learning rate for fast convergence, fine-tuning in later stages).

## MLOps Practices: Experiment Management and Monitoring

The project integrates the Weights & Biases (wandb) platform to real-time track system metrics such as training/validation loss curves and GPU utilization, record hyperparameter configurations, and support experiment comparison and version management. This tool helps developers detect training anomalies in time and improve collaboration and iteration efficiency.

## Project Structure: Clear Code Layered Design

The code is layered by function: src/ contains data loading (dataset.py), network definition (model.py), and training loop (train.py); configs/ stores YAML hyperparameter configurations; scripts/ provides training startup scripts; notebooks/ are used for data analysis and visualization. Structured organization improves readability and maintainability.

## Experimental Results and Effect Verification

The model was trained for 100 epochs and achieved an accuracy of 98.72% on the test set. The regularization strategy enhanced generalization ability, and dynamic learning rate scheduling ensured sufficient convergence of optimization, proving that well-designed architectures and strategies can achieve excellent results in specific domains.

## Application Expansion and Future Recommendations

Potential expansion directions for the project include: collecting more disease images from different regions/crops at the data level; trying deep networks or attention mechanisms at the model level; converting to TensorRT/ONNX to optimize inference speed at the deployment level. Its MLOps practices are of reference value for various machine learning projects.

## Project Summary

This project demonstrates the complete workflow of a deep learning project: from problem definition, data preparation, model design to training optimization and experiment management. Even without using pre-trained models, high accuracy can still be achieved through careful design, providing an excellent learning case for understanding CNN principles and MLOps practices.