Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for Pest and Disease Detection: Build an Image Classifier is a practical, project-driven course that ends with a working model. You learn the full pipeline of image classification applied to plant health: collecting and labelling images of pests and diseases, augmenting data, training a convolutional or transfer-learning model, and evaluating and improving it. The course keeps the focus on doing — building a classifier you can deploy in the field — and on the practical pitfalls of real agricultural imagery. You finish with a working pest-and-disease classifier and the skills to build more. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This hands-on course teaches you to build an image classifier for pest and disease detection — from dataset to trained model — using deep learning on plant images.
📋 Course Objectives
1. Collect and label plant-image datasets.
2. Apply data augmentation.
3. Train CNN and transfer-learning classifiers.
4. Evaluate and improve model accuracy.
5. Prepare a model for field use.
👥 Who Should Enroll?
• Agri-tech developers and data scientists
• Plant-science and crop professionals
• ML beginners wanting a real project
• Students of applied AI
🚀 Key Learning Outcomes
• A working pest-and-disease image classifier.
• Hands-on deep-learning skills.
• An agricultural-AI project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Visual Computing Fundamentals and AI Foundations
Develop a comprehensive understanding of visual computing concepts and their applications in AI for pest and disease detection • Analyze the fundamentals of computer vision and machine learning to build a strong foundation for image classification • Design and implement basic image processing techniques using Python and OpenCV to enhance image quality and prepare datasets
Image Processing, Augmentation, and Feature Extraction
Implement image augmentation techniques to increase dataset diversity and reduce overfitting in image classification models • Evaluate the effectiveness of various feature extraction methods, including convolutional neural networks (CNNs) and transfer learning • Configure and optimize image processing pipelines using Python and scikit-image to improve image classification accuracy
CNN Architectures, Transfer Learning, and AI Models
Design and implement CNN architectures using TensorFlow and Keras to classify pest and disease images • Analyze the performance of transfer learning models, including VGG16 and ResNet50, for image classification tasks • Develop and evaluate custom CNN models using Python and PyTorch to improve image classification accuracy
Object Detection, Segmentation, and Localization
Implement object detection algorithms, including YOLO and SSD, to detect pests and diseases in images • Evaluate the effectiveness of image segmentation techniques, including U-Net and Mask R-CNN, for pixel-level classification • Configure and optimize object detection and segmentation pipelines using Python and OpenCV to improve detection accuracy
Video Analysis, Temporal Models, and Real-Time Processing
Develop and implement video analysis pipelines using Python and OpenCV to detect pests and diseases in real-time • Analyze the performance of temporal models, including LSTM and GRU, for video classification tasks • Configure and optimize real-time processing pipelines using Python and PyTorch to improve video analysis accuracy
Model Optimization, Quantization, and Edge Deployment
Implement model optimization techniques, including pruning and knowledge distillation, to reduce model size and improve inference speed • Evaluate the effectiveness of model quantization methods, including post-training quantization and quantization-aware training • Configure and deploy optimized models on edge devices using Python and TensorFlow Lite to improve real-time processing performance
Industry Applications and AI Use Cases
Develop and implement AI-powered solutions for pest and disease detection in various industries, including agriculture and forestry • Analyze the effectiveness of AI models in real-world applications and identify areas for improvement • Design and propose novel AI-powered solutions for emerging industry challenges and applications
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | OpenCV |
| Covered Tool / Platform | scikit-image |
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