🌾 Demystifying CNN Training Using ResNet50 for Agricultural Automation

How can deep learning revolutionize agriculture? With the power of computer vision and models like ResNet50, it’s now possible to automate tasks like plant health monitoring, species identification, and disease detection—directly in the field.

In this post, I’ll explain how we built and trained a custom CNN model using ResNet50 for an agricultural automation project—simplified for beginners and practitioners alike.

🧠 What is ResNet50 and Why Use It?

ResNet50 is a deep Convolutional Neural Network (CNN) with 50 layers, known for solving vanishing gradient problems using skip connections (residuals). It’s powerful yet efficient—ideal for image classification tasks with limited datasets.

In our project, we used ResNet50 to:

  • Identify cotton plants from images
  • Monitor plant health conditions
  • Automate detection for precision farming

🛠️ Project Stack at a Glance

Component Tool/Tech Used
📷 Dataset Collection OpenCV-Python
🏷️ Annotation RoboFlow
🔄 Augmentation Image flipping, rotation, contrast changes
🧪 Model ResNet50 (pretrained on ImageNet)
🧠 Framework TensorFlow & Keras
📊 Evaluation Accuracy, Confusion Matrix

🔍 Data Collection and Preprocessing

We captured real-world images using OpenCV and annotated them with RoboFlow. To avoid overfitting, we used image augmentation to:

  • Increase dataset size by 40%
  • Introduce variance in lighting, orientation, and scale

Each image was resized to 224x224, normalized, and split into training/validation/test sets.

🧑‍🔬 Training the Model

We fine-tuned ResNet50 using transfer learning:

  • Froze early layers to retain ImageNet features
  • Retrained final layers on our custom dataset
  • Used Adam optimizer and categorical_crossentropy loss

✅ Result: We achieved 99.31% accuracy on our validation set!

🧪 Key Learnings

  • Transfer learning accelerates training and improves accuracy with limited data
  • Data augmentation is crucial for real-world variability
  • Model evaluation needs more than accuracy—use confusion matrix to identify misclassifications

🚜 Real-World Impact

This model powers an autonomous cotton plant management vehicle—designed to reduce manual labor and improve crop monitoring efficiency.

In future iterations, we plan to integrate:

  • Edge deployment with Raspberry Pi
  • Multiclass classification (e.g., diseases, stages of growth)
  • Real-time feedback via IoT integration

🛤️ What’s Next for Me

I’m continuing to:

  • Explore model optimization for edge devices
  • Work on open datasets for agriculture
  • Write more technical content to share these learnings

This project showed me how deep learning + agriculture can solve real-world problems in developing regions.

📚 Resources to Get Started

💬 Let’s Connect!

Are you working on AI in agriculture? Or learning CNNs for real-world use cases?

Github : https://github.com/Pj-develop

Drop a comment or connect—we’d love to exchange ideas 🌱