Artificial Intelligence (AI) is the intelligence of machines or software as against to the intelligence of humans. AI makes machines that can think like humans. AI technologies process large amounts of data in ways, unlike human and when embedded with other digital technologies, AI can make the system “smart”. AI has the vast applications in agriculture from weather predictions, monitoring the crop health, determine the optimal irrigation schedule; and nutrient application timings, recommend suitable agronomic practices to pest and disease management and many more. As such these are future farming technologies. However there is need to deliberate on the potential of AI in agriculture. The electromagnetic radiation reflected, absorbed, or transmitted by the material at different wavelengths is known as spectral response or characteristics. As each material reflects, absorbs, or transmits light in a unique way depending on its surface properties; the spectral response is unique and hence it is also termed as the spectral signature. These unique spectral signatures of the material can be exploited for various applications. The traditional imaging captures the information that human eyes can see (visual range) i.e. in three bands of color (red, green, and blue). However, the hyperspectral imaging is a technique that involves capturing and processing information from across the electromagnetic spectrum in many more bands, often spanning the ultraviolet, visible, and infrared regions of the spectrum that human eyes cannot see. In hyperspectral imaging, each pixel in an image contains a spectrum of information rather than just color. This allows for a more detailed analysis of the objects or scenes being imaged. By analyzing the spectral characteristics of objects or surfaces, it is possible to identify and classify materials, monitor changes, and gain insights into the composition and health of different surfaces. The ability of the hyperspectral imaging to capture detailed spectral information across a broad range of wavelengths makes this technique a powerful tool for various scientific, industrial, and commercial applications. It enables more appropriate analysis and interpretation of the data compared to traditional imaging methods; and hence being used for various applications in remote sensing, medical imaging, food quality inspection, forensic, mining; and military and defense. Hyperspectral imaging also offers a wide range of applications in agriculture, leveraging its ability to capture detailed spectral information from crops and soil. Some of the key applications include: land cover mapping, pest and disease detection, identification of nutrient deficiency, weed detection, biomass and yield estimation, drought monitoring, irrigation scheduling, soil moisture monitoring and genetic research. Hyperspectral imaging has the capability of early detection of abiotic and biotic stresses in plants and hence this technique has the potential for the development of early warning systems in agriculture. More importantly the ability of the spectral sensors to integrate with IoT enabled system makes the hyper-spectral imaging the significant applications for the real time, climate smart and precision agriculture. In order to investigate the AI and hyperspectral imagining technologies and explore further their various applications in agriculture for real-time, climate smart and precision agriculture; and to make the students, researchers, practioners, and industries aware about the advances in AI and hyperspectral imaging technologies; and bring them on one platform to deliberate on several aspects of AI and hyperspectral imaging technologies, the “International Symposium on AI and Hyperspectral Imaging for Agriculture” is organized with following specific objectives.