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Integration of Artificial Intelligence (AI) towards more sustainable agricultural products.

As centuries go by, it has made itself clear that machines are as quick-witted as humans or even better, it can predict how the future looks like for the next century. Our generation is blessed with numerous tools and gadgets that ease our daily lives within the simple touch of our own fingertips thus showing how miraculous innovations have come so far. As we move forward with our progressive society, us humans simply could not resist in integrating conventional farming with these modern technologies and tools. Agriculture specifically is currently grappling with numerous challenges such as climate change, water scarcity, environmental degradation, and a reliance on conventional energy sources (Betts et. al, 2017, Qi et. al, 2018) . Traditional farming methods often lead to reduced biodiversity, pollution, and health risks for humans and animals. To address these issues, there is a global push towards transforming agriculture through modern automated approaches that treat farms as production units and emphasize economic, social, and environmental sustainability (Godfray & Garnett, 2014). 


AI-driven agriculture is crucial in enhancing precision and promoting sustainable farming practices. By utilizing AI algorithms, farmers can better manage irrigation and conserve water. AI also automates labor-intensive tasks such as harvesting, pruning, and plowing, using autonomous tractors and harvesters, which reduces the need for human labor (Nikolidakis et. al, 2015). Addressing the dual challenges of a growing population and widespread hunger requires an integrated approach to issues like soil fertility, water scarcity, energy insecurity, and crop and animal diseases. Sustainable agriculture incorporates methods that produce food in a way that is ecologically, economically, and socially balanced. The concept of "secured smart sustainable agriculture (SSSA)" combines various elements to ensure the efficiency, sustainability, and security of agriculture. This integrated framework includes practices like crop rotation and organic farming to increase yields while minimizing environmental impact, as well as robust data security measures to protect agricultural data collected via sensors, drones, and Internet-of-Things (IoT) devices (Lu et. al, 2017).


Figure 1 Main pillars of Secured Smart Sustainable Agriculture from Sustainable AI-based production agriculture: Exploring AI applications and implications in agricultural practices (Mana et al, 2024)


Furthermore, the integration of blockchain technology, precision agriculture, renewable energy, and IoT-based solutions is revolutionizing the agricultural sector. Blockchain's decentralized and immutable ledgers ensure transparency and traceability in supply chains and financial transactions, safeguarding their integrity. Precision agriculture leverages advanced technologies like GPS, sensors, and analytics to enable real-time decision-making, reducing resource waste and boosting crop yields. Incorporating renewable energy sources such as solar panels and wind turbines into farming operations helps reduce carbon emissions and energy costs. Meanwhile, IoT-based solutions connect various devices and sensors for real-time monitoring, facilitating data-driven decisions and promoting more efficient and secure agricultural practices Parekh et. al, 2019).


Data-driven agriculture is emerging as a powerful solution to global food security challenges. By leveraging data from sensors, drones, tractors, weather stations, and satellite imagery, farmers can significantly increase productivity and reduce food losses. According to the International Food Policy Research Institute, these techniques could boost farm productivity by up to 67% by 2050. However, high costs and lack of digital infrastructure pose barriers, especially for smallholder farmers in Asia who produce over 80% of the region's food. Affordable internet connectivity is essential for collecting and utilizing farm data effectively (Dimiduk et. al, 2018). By using this data to guide farming practices, farmers can achieve higher yields with fewer inputs and less environmental impact. Despite the potential economic value of over $100 billion in Southeast Asia, the fragmented nature of agri-food systems makes it challenging to fully harness big data. For inclusive growth, it is crucial to empower smallholder farmers to participate in modern agricultural value chains (Linaza et. al, 2021).


However, Artificial Intelligence (AI) has a notable environmental impact, mainly due to its significant carbon footprint. Data centers, which house the servers storing and processing AI data, consume vast amounts of energy for both computational power and cooling systems to prevent overheating. These centers currently use about 1% of the world's electricity, a figure expected to rise with the growing demand for AI applications (Parekh et. al, 2019). Much of this energy comes from non-renewable sources, contributing to greenhouse gas emissions, and the cooling process often uses water that cannot be reused. Additionally, manufacturing the hardware for these data centers involves extracting rare earth metals and generates considerable electronic waste. To balance AI's benefits with its environmental costs, it is essential to adopt energy-efficient algorithms, optimize data center operations, and consider the entire lifecycle of AI hardware to reduce its carbon footprint Keshta, 2022).


Artificial intelligence (AI) has emerged as a key player in this transformation, offering innovative solutions for monitoring and optimizing crop production, soil health, and livestock management. AI applications in agriculture include real-time crop monitoring, efficient water usage, and the deployment of robots and drones for tasks like weeding, pest detection, and crop quality assessment. Studies have shown that AI and machine learning can significantly enhance sustainable farming practices, ensuring food security and environmental conservation. The investment in AI technologies in agriculture is expected to grow substantially, highlighting the sector's shift towards smart agriculture as a means to meet global food demands while preserving the ecosystem.

 

Article prepared by: Nur Anis Elias, MBIOS R&D Associate 23/24


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References

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