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Online Certified CourseApplications of Artificial Intelligence in Agriculture
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Course Modules
Unit 01 Artifiical Intelligence Meets Plant Health
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Unit 02 Ai Based Plant Disease Detection
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Unit 03 Pest Prediction Using Artificial Intelligence
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Unit 04 Disease Prediction And Forecasting Using Ai
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Unit 05 Image Based Disease Diagnosis And Computer Vision
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Unit 06 Crop Health Monitoring Using Artificial Intelligence
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Unit 07 Decision Support Systems (dss) & Early Warning Systems
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Unit 08 Future Trends, Challenges And Case Studies
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Learning Objectives
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Understand the role of AI in modern agriculture and plant health management.
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Identify plant diseases and pests using AI-based techniques.
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Apply image processing and computer vision concepts for disease diagnosis.
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Understand AI-based disease prediction, forecasting, and early warning systems.
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Explore AI-driven decision support systems and future agricultural innovations.
Why Enroll in This Course?
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What You'll Learn in Each Module
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AI Meets Plant Health: Explore AI applications in agriculture, plant health monitoring, and precision farming.
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AI-Based Plant Disease Detection: Understand machine learning and deep learning approaches for identifying plant diseases.
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Pest Prediction Using AI: Learn how AI analyses environmental and crop data to predict pest outbreaks.
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Disease Prediction and Forecasting Using AI: Explore predictive models, disease forecasting, and early warning techniques.
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Image-Based Disease Diagnosis and Computer Vision: Understand image processing, image classification, and computer vision for automated disease diagnosis.
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What You'll Learn in Each Module
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Overview & Objectives
Applications of Artificial Intelligence in Agriculture
Structured online learning designed to help learners build practical speaking, presentation and interpersonal ability.
About This Course
This course course explores the applications of Artificial Intelligence (AI) in modern agriculture, with a special focus on plant health, disease detection, and pest prediction. Learners will understand how AI, machine learning, and computer vision help identify crop diseases, monitor plant health, and forecast agricultural risks. The course combines theoretical concepts with practical applications, case studies, and emerging trends to develop industry-relevant skills in AI-driven precision agriculture.
Meet Your Trainer
T. Monika Singh
T. Monika Singh received her M.Tech degree in Computer Science and Engineering from Osmania University and is currently pursuing a Ph.D. in the Department of Information Technology at Annamalai University. Since 2017, she has been serving as a faculty member in the Department of Computer Science and Engineering at Stanley College of Engineering and Technology for Women. She has extensive experience in both teaching and research and is certified as a Project-Based Learning (PBL) mentor by Wipro, in addition to holding a TalentNext certification in Java Full Stack Development. Her primary research interest is in the field of Machine Learning, and she has authored many research publications indexed in Scopus, along with several other papers published in reputed national and international journals and conferences. She also holds a patent to her name and is an active member of the Association for Computing Machinery (ACM), demonstrating a strong commitment to advancing technological education and innovation.
