The earth’s natural land cover has gone through significant changes because of population growth and urbanization which in the end led to negative impacts on the environment and triggered the climate crisis . Desertification is a major concern with 12 million hectares of fertile land turning into deserts every year . The Intergovernmental Panel on Climate Change (IPCC) warns of the disastrous consequences of climate change including threats to food production, water resources and the survival of ecosystems .
Taking measures are crucial to combat these challenges . The IPCC report stresses the importance of water management and ecosystem restoration . Emphasizing equity and social justice is also crucial for effective adaptation efforts .
Leveraging AI for Efficient Agronomy Management
Addressing these challenges requires the adoption of Good Agricultural Practices (GAP) which prioritize sustainable land cover and food safety . Agronomy management when assisted by ground-based and remote sensing technologies allows farmers to monitor changing conditions in their fields and manage crop production more efficiently .
Remote sensing satellites play a vital role in monitoring vegetation through electromagnetic energy interactions . Vegetation signals that are taken from satellite data such as NDVI and EVI provide insights into plant phenology and crop yields . Artificial intelligence (AI) algorithms increase agronomy management by forecasting crop production and optimizing planting and harvest dates .
Blockchain’s Role in Ensuring Food Safety and Supply Chain Efficiency
Decentralized blockchain technology also offers opportunities in agriculture . Blockchain can improve supply chain efficiencies, traceability and food safety protocols . By ensuring transparency and certification throughout the production process from seed to final product blockchain enhances food product traceability and supply chain management .
A project demonstrated the potential of AI algorithms and satellite imagery in predicting land changes that are caused by urbanization . The project successfully classified agricultural and residential areas in the Marmaris region by applying deep neural networks to satellite images . The project also explored the use of blockchain driven collective learning in which stakeholders could collaboratively train machine learning models without having to share sensitive data .
The combination of AI and blockchain technologies holds promise for sustainable agriculture, climate adaptation and combating land degradation . By embracing good agricultural practices leveraging AI for agronomy management and implementing blockchain for supply chain efficiency the agricultural sector can address the challenges that are posed by climate change and ensure a more sustainable future .
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