Course

Syllabus

Theory

Introduction to digital agriculture; digital transformation in agriculture – Agriculture 4.0 and Agriculture 5.0. Fundamentals of Internet of Things (IoT); IoT architecture and communication protocols; sensors and actuators; types of agricultural sensors for soil moisture, temperature, humidity, rainfall, pH, electrical conductivity, nutrient status, leaf wetness, light intensity, and weather monitoring; wireless sensor networks; cloud computing, edge computing, and data acquisition systems for agriculture.

Fundamentals of Unmanned Aerial Vehicle (UAV); UAV components and classifications; remote sensing principles; RGB, multispectral, hyperspectral, and thermal sensors; image acquisition, processing, and interpretation; drone applications in crop monitoring, nutrient management, irrigation scheduling, pest and disease detection, yield estimation, and crop health assessment; DGCA guidelines and safety regulations.

Introduction to agricultural robotics and automation; autonomous field machinery; robotic systems for seeding, transplanting, weeding, harvesting, spraying, and post-harvest operations; automation in protected cultivation and irrigation systems; Artificial Intelligence (AI), Machine Learning (ML), Computer Vision, Digital Twins, and Internet-enabled farm management systems; opportunities, challenges, ethics, data security, and future trends in digital agriculture.

Practical

Introduction to digital agriculture platforms and smart farming technologies; identification and calibration of agricultural sensors; installation and operation of IoT-based weather stations and soil monitoring systems; interfacing sensors with Arduino/Raspberry Pi; collection, storage, and visualization of sensor data.

Demonstration of wireless communication modules for IoT applications; operation and mission planning of agricultural drones; acquisition and processing of UAV images; vegetation indices (NDVI and related indices) for crop health assessment; GIS-based visualization of drone data.

Demonstration of robotic and automated agricultural equipment; automation of irrigation systems using sensors and controllers; monitoring crop and environmental parameters using IoT dashboards; case studies on digital agriculture technologies; preparation of precision farming recommendations based on sensor and drone data; field visit to digital farms, precision agriculture facilities, or drone service providers.

Objectives

1. To impart fundamental knowledge of digital agriculture concepts, precision farming technologies, and smart agricultural systems.

2. To familiarize students with Internet of Things (IoT), sensors, drones, robotics, automation, and data acquisition techniques for agricultural applications.

3. To develop skills in integrating digital technologies for crop monitoring, resource management, decision support, and sustainable agricultural production.

Course Outcome:

Any student who has undergone the course shall be able to:

Suggested Readings

1. Castrignanò, A., Buttafuoco, G., Khosla, R., Mouazen, A., Moshou, D., & Naud, O. (Eds.). (2020). Agricultural internet of things and decision support for precision smart farming. Academic Press.

2. Karkee, M., & Zhang, Q. (Eds.). (2021). Fundamentals of Agricultural and Field Robotics. Springer Nature, https://doi.org/10.1007/978-3-030-70400-1

3. Kateris, D., Benos, L., Bochtis, D., et al. (2023). Unmanned Aerial Systems in Agriculture: Eyes Above Fields. Academic Press (Elsevier). DOI: 10.1016/C2020-0-04638-4

4. DGCA Digital Sky Platform (Drone Regulations) – https://digitalsky.dgca.gov.in

5. Pix4D Documentation – https://support.pix4d.com

6. Arduino Documentation – https://docs.arduino.cc

7. Raspberry Pi Documentation – https://www.raspberrypi.com/documentation