Artificial Intelligence (AI) has the potential to revolutionize Nigeria’s economy by addressing corruption, infrastructural deficiencies, and inefficiencies in the agricultural sector, according to experts.
With the global population expected to exceed 9 billion by 2050, agricultural and food production must increase by 70 per cent to meet the demand. This challenge underscores the critical need for sustainable agricultural practices to ensure food security and eradicate hunger.
AI, a powerful tool simulating human intelligence and processes, is being harnessed in agriculture through applications such as natural language processing (NLP), computer vision, and expert systems. These technologies analyze vast amounts of data, enabling actionable insights and informed decision-making.
Nigeria has a unique opportunity to leverage AI for economic transformation. By embracing AI and investing in its responsible development, the country can unlock significant economic potential, fostering a more prosperous and inclusive future for its citizens.
One of the key pillars of this digital revolution in agriculture is the deployment of Internet of Things (IoT) solutions across farming landscapes. IoT-enabled sensors embedded in soil and crops provide real-time data on moisture levels, nutrient content, and disease prevalence. This information, analyzed through interconnected systems, allows farmers to make informed decisions regarding irrigation schedules, fertilizer application, and disease management.
IoT-enabled precision agriculture extends beyond basic monitoring to include predictive analytics and automated control systems. By analyzing data on environmental factors, crop health indicators, pest infestations, and equipment performance, farmers can proactively address challenges and optimize interventions. Predictive models, for instance, can forecast disease outbreaks based on weather patterns, enabling targeted pest control or the selection of disease-resistant crop varieties.
A significant advantage of AI in agriculture is its ability to enhance resource efficiency and sustainability. By precisely monitoring soil conditions, water usage, and nutrient levels, farmers can optimize inputs such as fertilizers and irrigation, reducing waste and environmental impact. This conservation of resources also leads to cost savings and promotes long-term soil health, which is crucial for sustainable farming practices.
Machine Learning (ML) algorithms are increasingly important across the main four clusters of the agriculture supply chain: preproduction, production, processing, and distribution. In preproduction, Machine Learning technologies predict crop yield, soil properties, and irrigation requirements. During production, Machine Learning aids in disease detection and weather prediction. In processing, Machine Learning estimates production planning to ensure high-quality products. In distribution, Machine Learning optimizes storage, transportation, and consumer analysis.
Despite its benefits, AI technology poses challenges. The most significant social challenge is potential unemployment, as smart machines and robots could replace many repetitive tasks, reducing human involvement and impacting employment standards. Technologically, AI systems can only perform tasks they are programmed to do; anything beyond that may result in crashes or irrelevant outputs.
AI integration extends beyond on-farm operations to encompass supply chain optimization and market intelligence. AI-driven logistics and inventory management systems optimize storage conditions, transportation routes, and distribution networks for agricultural products, reducing waste and ensuring timely delivery to markets. Additionally, AI-powered market analysis tools provide farmers with insights into price trends, demand fluctuations, consumer preferences, and market opportunities, empowering strategic marketing and pricing decisions.
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The agriculture and food industries are vital to humanity, with agricultural products serving as inputs in multi-actor distributed supply chains, including preproduction, production, processing, and distribution stages. Given the future challenges in the agriculture and food sector, such as climate change, population growth, technological progress, and natural resource constraints, it is urgent to employ digital technologies. These include automation of farm machinery, use of sensors and remote satellite data, AI, and ML for improved crop monitoring and water management, ensuring traceability of agricultural food products.
