AI, ML & IOT Hands-on in Agriculture
Mastering Algorithms for the Future of Farming.” Target Audience: Post-Docs, Professors, Research Scientists. Platform: Google Colab (Python) + Scikit-Learn + TensorFlow/Keras.
About This Course
This intensive 3-day workshop bridges the gap between agronomy and data science, designed specifically for researchers and industry professionals. You will move beyond traditional statistics to master Machine Learning, Computer Vision, and IoT data processing using Google Colab. Through hands-on coding, you will build predictive models for yield and disease detection, equipping you with the tools to modernize your research and operationalize smart farming solutions.
Aim
To empower agricultural researchers and professionals with the practical skills to independently apply Machine Learning and IoT analytics in their domain. The workshop aims to bridge the digital skills gap, enabling participants to transition from traditional statistics to advanced predictive modeling and computer vision. Ultimately, attendees will leave with the capability to build, train, and deploy AI models that solve real-world farming challenges and enhance research outcomes.
Workshop Objectives
- Demystify Artificial Intelligence: To strip away the jargon surrounding AI and Machine Learning, presenting them as accessible, practical tools for agricultural problem-solving.
- Bridge the Interdisciplinary Gap: To provide domain experts (agronomists, biologists) with the computational literacy required to collaborate effectively with data scientists and engineers.
- Enable Data-Driven Decision Making: To transition participants from relying on manual observation and intuition to utilizing data-backed predictive models for crop management.
- Operationalize IoT Data: To teach the specific methodologies for cleaning, processing, and analyzing high-velocity time-series data from field sensors.
Workshop Structure
Day 1: The Decision Engine (Tabular Data)
Goal: Ingest soil data and train a classifier to recommend crops.
- Phase 1: Data Ingestion & “Smart” Cleaning
- Code: Import
Pandasand load theSoil_Nutrients.csv. - Action: Handle missing values (imputation) and map text labels (e.g., “Rice”, “Maize”) to numbers using
LabelEncoder. - Output: A clean, numeric feature matrix ready for AI.
- Code: Import
- Phase 2: Training the Random Forest
- Code: Import
RandomForestClassifierfrom Scikit-Learn. - Action: Split data (80% Train / 20% Test). Write the training logic:
model.fit(). - Closure: Run a single prediction command on a specific soil sample to prove the model works.
- Code: Import
Day 2: The Vision System (Images)
Goal: Process leaf images and retrain a pre-existing AI model to detect disease.
- Phase 1: The Image Pipeline
- Code: Import
TensorFlowandImageDataGenerator. - Action: Create a pipeline that automatically resizes and scales raw images from the dataset folder into a format the neural network accepts (224×224 pixels).
- Code: Import
- Phase 2: Transfer Learning Implementation
- Code: Load the pre-trained MobileNetV2.
- Action: Freeze the “base” layers (so we don’t retrain the whole brain) and add a custom “Output Layer” for our specific disease classes.
- Closure: Execute
model.compile()and run 5 epochs of training. Watch the accuracy metric rise in real-time.
Day 3: The Forecaster (IoT/Time-Series)
Goal: Format sensor logs and generate a future forecast line.
- Phase 1: Time-Series Formatting
- Code: Import
Prophet(orNeuralProphet). - Action: Convert standard sensor logs into the specific
ds(Datestamp) andy(Value) format required by forecasting engines. Resample noisy data to hourly averages.
- Code: Import
- Phase 2: Generating the Forecast
- Code: Initialize and fit the Prophet model:
m.fit(df). - Action: Create a “future dataframe” placeholder for the next 7 days.
- Closure: Call
model.predict(future)and plot the trend line to see the predicted moisture drop.
- Code: Initialize and fit the Prophet model:
Who Should Enrol?
1. Target Audience
- Academic: Professors, Associate/Assistant Professors, and Guest Lecturers in Agriculture, Botany, or Environmental Sciences.
- Research: Ph.D. Scholars, Post-Doctoral Fellows, and M.Tech/M.Sc students specializing in Agronomy or Precision Farming.
- Industry: Agronomists, Soil Scientists, and Professionals working in Ag-Tech or Government Agricultural Departments.
2. Technical Knowledge
- Coding: No prior programming experience is required. The workshop follows a “Zero-to-Code” approach.
- Mathematics: A basic understanding of statistical concepts (Mean, Median, Standard Deviation, Regression) is recommended.
- Data: Familiarity with handling data in Microsoft Excel or CSV formats.
3. System Requirements
- Hardware: A laptop with internet connectivity (Windows, Mac, or Linux).
- Account: A working Google (Gmail) account to access Google Colab.
- Browser: Latest version of Google Chrome or Firefox.
Important Dates
Registration Ends
01/28/2026
IST 4:30 PM
Workshop Dates
01/28/2026 – 01/30/2026
IST 5 : 30 PM
Workshop Outcomes
By the end of this workshop, participants will be able to:
- Build Predictive Models: Independently write and execute Python code in Google Colab to predict crop yields and classify soil health with measurable accuracy.
- Analyze Field Imagery: Construct and train Convolutional Neural Networks (CNNs) to automatically detect plant diseases and pests from drone or smartphone images.
- Process Sensor Streams: Ingest and clean real-world IoT data, handling common field issues like missing values, noise, and connectivity gaps.
- Deployable Assets: Walk away with a portfolio of reusable code notebooks (GitHub/Drive) that can be immediately adapted to their own specific research data and grant projects.
Fee Structure
Student
₹2999 | $65
Ph.D. Scholar / Researcher
₹3999 | $75
Academician / Faculty
₹4999 | $85
Industry Professional
₹5999 | $115
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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