New Year Offer End Date: 30th April 2024
Program
AI for Pest & Disease Detection: Build an Image Classifier
Hands-On Deep Learning for Crop Pest & Disease Detection
Skills you will gain:
About Program:
“AI for Pest & Disease Detection: Build an Image Classifier” is a fully hands-on, project-based workshop where participants learn how to turn simple leaf photos into an AI-powered decision-support tool for farmers. Using Google Colab, Python, and transfer learning (e.g., MobileNet/ResNet), you will work step-by-step with real plant health images to build, train, and test an image classifier that can flag likely diseases and suggest advisory messages.
Aim: This hands-on workshop aims to equip participants with the practical skills to build, evaluate, and responsibly use an AI-based image classifier for crop pest and disease detection—starting from raw leaf images and ending with a simple “farmer-facing” decision-support flow.
Program Objectives:
- Introduce participants to the fundamentals of AI-based pest and disease detection using leaf images.
- Enable participants to explore, organise and assess the quality of plant health image datasets in Google Colab.
- Guide participants in building a transfer learning–based image classifier for crop pest/disease classes.
- Develop the ability to interpret model predictions (class + confidence) and link them to simple advisory messages for farmers.
- Create awareness about the risks, limitations and biases of image-only AI diagnosis, and the need for human expert validation.
- Encourage participants to design a practical deployment flow (app/WhatsApp bot + agronomist) for real-world extension use.
What you will learn?
📅 Day 1 – Data & Image Classification Basics (Hands-on)
- Problem framing: farmer takes a picture → app gives “likely disease + advice”.
- Hands-on: mount Google Drive in Colab, list images, show sample images with matplotlib.
- Image quality discussion: lighting, angle, noise and their effect on model performance.
- Mini dataset tour: folder structure (healthy/, diseased/ or 3–4 classes).
- Quick reflection: limitations of AI purely from images.
- Hands-on take-home: Colab notebook 01_pest_image_dataset_exploration.ipynb + folder with labelled training images.
📅 Day 2 – Build & Train a Simple Classifier (Transfer Learning, Hands-on Lab)
- Concept input: what is transfer learning and why it helps with small datasets.
- Hands-on: load a pre-trained model (e.g., MobileNet/ResNet) and freeze base layers.
- Model head: add a small dense classification layer for pest/disease classes.
- Training lab: train for a few epochs and view accuracy/loss curves.
- Hands-on quick test: upload a new leaf image and see the predicted class.
- Hands-on take-home: Colab notebook 02_pest_classifier_training.ipynb + first working “AI diagnosis” model (even if rough).
📅 Day 3 – Responsible Use & Deployment Ideas (Hands-on Design & Reporting)
- Discussion: error, bias & impact – misclassification → wrong spraying advice and farmer risk.
- Hands-on: add a prediction report cell (predicted class, confidence, advisory template text).
- Hands-on design exercise: sketch an app/WhatsApp bot flow using this model.
- Human in the loop: integrate agronomist/extension worker validation into the flow.
- Closing: how to extend – more classes, multilingual advisory, integration with helplines.
- Hands-on take-home: Colab notebook 03_pest_classifier_reporting_and_flow.ipynb + app-flow diagram for project documentation.
Mentor Profile
Fee Plan
INR 1999 /- OR USD 50
Get an e-Certificate of Participation!
Intended For :
- Undergraduate and postgraduate students in Agriculture, Agricultural Engineering, Plant Pathology, Biotechnology, Data Science or related fields.
- Researchers and faculty members interested in applying AI/ML or computer vision to crop health and pest/disease management.
- Professionals from agri-input companies, agri-tech startups, FPOs, NGOs and extension services working with farmers.
- AI/ML enthusiasts looking for a practical, agriculture-focused image-classification project.
- Preferred background: Basic familiarity with any programming language (Python is a plus) and comfort with using Google Colab.
Career Supporting Skills
Program Outcomes
- Understand how pest and disease image datasets are structured (classes, folders, data quality).
- Use Google Colab to explore, visualise and manage plant health images (dataset exploration notebook).
- Build and train an image classifier for crop pest/disease detection using transfer learning (e.g., MobileNet/ResNet).
- Evaluate model performance using accuracy/loss curves and simple test images.
- Generate a basic prediction report including class, confidence and advisory template text.
- Design a responsible deployment flow (app or WhatsApp bot) with human agronomist validation in the loop.