Information Processing in Agriculture

Information Processing in Agriculture (IPA) was established in 2013 to encourage the development of science and technology related to information processing in agriculture, through the following aims:

• Promote the use of knowledge and methods from information processing technologies in agriculture;
• Report on experiences and publications of institutes, universities, and government, as well as profitable technologies for agriculture;
• Provide a platform and opportunities for exchanging knowledge, strategies, and experiences among information processing researchers worldwide;
• Promote and encourage interactions among agriculture scientists, meteorologists, biologists (pathologists/entomologists), information technology professionals, and other stakeholders to develop and implement methods, techniques, and tools related to information processing technology in agriculture;
• Create and promote expert groups for the development of agro-meteorological databases, crop and livestock modeling, and applications for the development of crop performance-based decision support systems.

Topics of interest include, but are not limited to, the following aspects:

• Smart sensors, biosensors and bioelectronics, material and molecular innovations for chemical and biological sensing, sensors, and automation and control systems for agriculture;
• Wireless sensor networks, 4G, NB-IOT, and 5G applications in agriculture;
• Remote sensing and discrete element modeling (DEM) applications in agriculture;
• Simulation, optimization, modeling, and automated control;
• Decision support systems, intelligent systems, and artificial intelligence;
• Machine vision, computer vision, image processing and automation, and imaging technologies for high-throughput phenotyping
• Advances in spectroscopy and hyperspectral properties of biological products;
• Advanced computational approaches for solving agricultural and biological engineering problems;
• Computational fluid dynamics (CFD) applications in agriculture;
• Inspection and traceability for food quality;
• Precision agriculture, intelligent instruments, robotics, and co-robotics for agriculture;
• Internet of things, cloud computing, and precision farming;
• Big data, data mining, and data analysis for agricultural applications;
• Unmanned aerial vehicles (UAVs) for sensing, imaging, and agricultural aquacultural applications.