AI-Enabled UAV Borne Hyperspectral Imaging for Crop-Livestock Farm Management

Le résumé fourni par la source

Crop-livestock farming plays a crucial role in global agricultural communities by integrating crop production with livestock farming to create a sustainable and diversified farming system. However, this industry has become increasingly scrutinized due to environmental impact, climate change, and land degradation. As per present reports, crop residues, a significant livestock feed resource, are in shortage and have poor nutritional value. Moreover, various factors like heatwaves, drought, and diseases can negatively impact forage quality and reduce productivity. Conventional methods of assessing forage/crop residue quality face significant challenges, including labor-intensive, costly, time-consuming, and error-prone. UAV-based imaging can boost multi-dimensional crop improvement programs due to advantages like wider coverage, short revising times, high spatial resolutions, and ease of operation. Hyperspectral imaging (HSI) sensors provide enriched spectral information, enabling more precise investigations into feed quality evaluation, forage management, and livestock health. Artificial intelligence and machine learning (AI/ML) approaches can effectively analyze high-dimensional HSI data and extract meaningful insights. Integrating UAV-based HSI and AI/ML techniques is crucial to enhance crop-livestock farm management. This chapter explores the potential of UAV-based HSI and AI/ML for crop-livestock farm research and management, focusing on animal and forage health monitoring, and enhancing feed quality. We also emphasize AI/ML-based data analytics and algorithm development on UAV-borne HSI data to revolutionize crop-livestock farming.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.