AI for Pest & Disease Detection: Build an Image Classifier

About This Course
AI for Pest & Disease Detection: Build an Image Classifier is a fully hands-on, project-based course 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 (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/pests and generate simple advisory messages.

Aim
This hands-on course equips participants with 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.

Course Objectives

• Introduce fundamentals of AI-based pest and disease detection using leaf images

• Enable dataset exploration, organisation, and quality assessment in Google Colab

• Guide learners to build a transfer learning–based classifier for crop pest/disease classes

• Interpret predictions (class + confidence) and map them to advisory messages for farmers

• Understand risks, limitations, and bias of image-only diagnosis + need for expert validation

• Design a practical deployment flow (app/WhatsApp bot + agronomist) for real extension use

Course Structure

✅ Module 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, visualise samples (matplotlib)

• Image quality discussion: lighting, angle, blur/noise and effect on performance

• Mini dataset tour: folder structure (healthy/, diseased/ or 3–4 classes)

• Quick reflection: limitations of AI purely from images
  👉 Outcome: Colab Notebook 01_pest_image_dataset_exploration.ipynb + labelled training image folder ready for modelling

✅ Module 2 – Build & Train a Simple Classifier (Transfer Learning Lab)

• Concept: transfer learning and why it works well for small datasets

• Hands-on: load pre-trained MobileNet/ResNet, freeze base layers

• Build model head: add classification layers for pest/disease classes

• Training lab: train for a few epochs + view accuracy/loss curves

• Quick test: upload a new leaf image and check predicted class
  👉 Outcome: Colab Notebook 02_pest_classifier_training.ipynb + first working “AI diagnosis” classifier (baseline model)

✅ Module 3 – Responsible Use & Deployment Ideas (Design + Reporting)

• Discussion: error, bias & impact (misclassification → wrong spraying advice and risk)

• Hands-on: generate prediction report (class, confidence, advisory template message)

• Design exercise: sketch an app/WhatsApp bot flow using the trained model

• Human-in-the-loop: add agronomist/extension validation step into workflow

• Extension ideas: more classes, multilingual advisory, helpline integration
  👉 Outcome: Colab Notebook 03_pest_classifier_reporting_and_flow.ipynb + deployment flow diagram for documentation

Who Should Enrol?

• UG/PG students in Agriculture, Agricultural Engineering, Plant Pathology, Biotechnology, Data Science, etc.

• Researchers & faculty applying AI/ML/computer vision to crop health

• Professionals from agri-input firms, agri-tech startups, FPOs, NGOs, extension services

• AI/ML learners who want a practical agriculture-focused image classification project

Preferred background (not mandatory):
Basic programming familiarity (Python is a plus) + comfort using Google Colab