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Deep Learning and Machine Learning for Agriculture

4.6 Basic to Advance 40 Hrs (20 Days, 2 Hours per Day) Very Soon

Instructor - Highly Proficient in Data Science, Machine Learning, and Agriculture Analytics

Course Overview

Gain a comprehensive understanding of Deep Learning (DL) and Machine Learning (ML) applied to agriculture in this 20-day intensive course. From soil nutrient prediction and rainfall forecasting to disease detection and plant health monitoring, you’ll master the techniques that are transforming the agricultural sector. Whether you’re interested in crop yield prediction, soil health analysis, or pest detection, this course will equip you with the tools to leverage ML and DL for real-world agricultural problems.

Why Enroll in This Course?

Master Advanced Techniques – Learn how to use Deep Learning and Machine Learning for agricultural problem-solving.
Hands-On Projects – Work on projects like soil nutrient prediction, disease detection, and plant growth forecasting.
Comprehensive Coverage – From the basics of Python programming to advanced deep learning architectures like YOLO and CNN, the course covers everything you need.
Learn to Deploy Models – Understand how to implement and evaluate ML and DL models for practical agricultural applications.
Cloud-Based Tools – Get hands-on experience using Google Colab, TPUs, and GPUs for training ML/DL models faster and more efficiently.

Who Should Take This Course?

Agriculture Analysts & Researchers – Enhance your skills in predictive modeling and disease detection.
Data Science & Machine Learning Enthusiasts – Learn to apply your ML/DL knowledge to real-world agricultural datasets.
Farmers & Agricultural Professionals – Use deep learning techniques to improve farm management and decision-making.
Students & Professionals in AgTech – Gain expertise in AI for agriculture and explore the future of farming technology.

Course Features

Live & Recorded Classes – Flexible learning with full-time access to recorded sessions.
Comprehensive Study Materials – Get PDFs, PPTs, and Python scripts for all modules.
Hands-On Projects – Work on real-world projects such as soil nutrient prediction, disease detection, and crop yield forecasting.
Multi-Device Access – Learn on mobile, laptop, or PC at your convenience.
1-Hour Demo Video – Get an introduction to ML/DL for agriculture and the tools you'll use throughout the course.

Course Outcomes

By the end of this course, you will be able to:

Develop and deploy ML/DL models for solving complex agricultural problems such as crop yield prediction, pest detection, and soil health analysis.
Preprocess and visualize agricultural datasets for predictive modeling and data analysis.
Use cloud computing resources such as Google Colab to run ML/DL models efficiently.
Implement and optimize deep learning architectures like CNN, YOLO, and MobileNet for tasks such as plant stress detection and disease classification.

Transform Your Career with Advanced AI & ML Skills in Agriculture!

Enroll Now and Master Deep Learning and Machine Learning for Agriculture!

Topics of Course

Basic Topic :

Introduction to Python for ML/DL

Basics of Python programming

Libraries: NumPy, Pandas, Matplotlib, and Scikit-learn

Introduction to Google Colab

Fundamentals of Machine Learning & Deep Learning

Overview of ML & DL concepts

Supervised vs. Unsupervised learning

Understanding Neural Networks and activation functions

Introduction to Data Preprocessing & EDA

Handling missing values and outliers

Feature selection and feature engineering

Data visualization using Matplotlib & Seaborn

Advance Topics :

Evaluation and Performance Metrics

Metrics for classification (Accuracy, Precision, Recall, F1-score)

Metrics for regression (R², RMSE, MAE)

Cross-validation and hyperparameter tuning

Introduction to Cloud Computing for ML/DL

Running models on Google Colab

Using GPUs and TPUs for faster computation

Introduction to cloud-based datasets

Predicting Soil Nutrients Using Hyperspectral Data

Basics of Hyperspectral Imaging (HSI)

Data preprocessing for hyperspectral analysis

Feature extraction from hyperspectral data

Training an ML model for soil nutrient prediction

Comparative Analysis of ML Models for Soil Nutrient Prediction

Comparing regression models (Linear Regression, Random Forest, XGBoost)

Evaluating model performance (R², RMSE, MAE)

Hyperparameter tuning and optimization

Soil Moisture Prediction using ML

Importance of soil moisture in agriculture

Dataset preparation and preprocessing

Implementing regression-based ML models (Random Forest, SVR, LSTM)

Depth-Wise Soil Moisture Prediction

Understanding soil moisture at different depths

Comparative analysis of regression vs. ML models

Model evaluation and visualization

Detection using Deep Learning

Image preprocessing for plant pest detection

Training CNNs for disease classification

Using transfer learning (VGG16, ResNet) for pest detection

YOLO-Based Weed Detection

Overview of YOLO object detection

Dataset preparation and annotation (Roboflow)

Training a YOLO model for weed detection

Soil Texture Classification

Basics of soil texture analysis

Feature engineering for soil texture classification

Implementing ML models (SVM, KNN, Decision Tree)

Soil pH Prediction Using ML

Importance of soil pH for crop health

Data collection and preprocessing

Regression-based ML models for soil pH prediction

Rainfall Prediction Using ML

Meteorological datasets and preprocessing

Training time-series models for rainfall forecasting (LSTMs, ARIMA)

Comparative analysis of ML-based predictions

Plant Stress Classification using CNN + MobileNet (Thermal Image)

Understanding Thermal Imaging for Plant Stress Detection

Building & Training a CNN with MobileNet for Stress Classification

Plant Growth Prediction using ML Models

Ensemble vs. non-ensemble ML models for plant growth prediction

Soil Nutrient Variability & Crop Yield

Exploring the impact of soil nutrients on crop yield

Ensemble models (AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost, RF)

Plant Health Prediction with ML

Exploring the plant health and application of ML

Tomato Disease Detection using Deep Learning

Overview of tomato plant diseases

Building CNN models

Comparing performance of different deep learning models

Dataset augmentation and transfer learning for tomato disease detection

Advanced Apple Disease Detection using Deep Learning

Overview of apple diseases (Apple Scab, Black Rot, Cedar Apple Rust, Healthy)

Training different CNN architectures (ResNet, InceptionV3, DenseNet, EfficientNet)

Data augmentation techniques for apple disease images

Transfer learning vs. training from scratch

Evaluating and deploying the best model

2199/-

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Course Name :-

Deep Learning and Machine Learning for Agriculture

Instructor:-

Highly Proficient

Category:-

Basic to Advanced

Level:-

Intermediate

Duration:-

40 Hours (20 Days, 2 Hours per Day)

Mode:-

Live & Recorded Sessions

Access:-

Mobile, Laptop, PC

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Requirements

At SkillBuilt 24x7, we welcome everyone who's ready to grow—no experience needed! Here's all you need to get started:

No need for basic coding knowledge – we start from scratch

Beginner-friendly – perfect for absolute newcomers

Just bring your curiosity and eagerness to learn new things

A laptop is a must – your gateway to hands-on learning

No prior concepts required – we’ll teach you everything step by step

Course

Deep Learning and Machine Learning for Agriculture

Course Overview

Why Enroll in This Course?

Who Should Take This Course?

Course Features

Course Outcomes

By the end of this course, you will be able to:

Transform Your Career with Advanced AI & ML Skills in Agriculture!

Enroll Now and Master Deep Learning and Machine Learning for Agriculture!

Topics of Course

Basic Topic :

Introduction to Python for ML/DL

Fundamentals of Machine Learning & Deep Learning

Introduction to Data Preprocessing & EDA

Advance Topics :

Evaluation and Performance Metrics

Introduction to Cloud Computing for ML/DL

Predicting Soil Nutrients Using Hyperspectral Data

Comparative Analysis of ML Models for Soil Nutrient Prediction

Soil Moisture Prediction using ML

Depth-Wise Soil Moisture Prediction

Detection using Deep Learning

YOLO-Based Weed Detection

Soil Texture Classification

Soil pH Prediction Using ML

Rainfall Prediction Using ML

Plant Stress Classification using CNN + MobileNet (Thermal Image)

Plant Growth Prediction using ML Models

Soil Nutrient Variability & Crop Yield

Plant Health Prediction with ML

Tomato Disease Detection using Deep Learning

Advanced Apple Disease Detection using Deep Learning

2199/-

Requirements

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