Food insecurity, a looming crisis threatening millions, is expected to escalate in the coming decades. Artificial intelligence (AI) technologies are increasingly pivotal in improving global food security by transforming farming practices and enhancing efficiency. This article deliberates on the importance of AI models in bolstering global food security.
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Importance of Food Security
Food security primarily describes the ability of households, communities, or people to access sufficient quantities of nutritious and affordable food reliably to meet their cultural and nutritional needs for active and healthy lives. Typically, food security is measured along four interrelated dimensions, including economic access to available food, physical availability of food supply, utilization, and the stability of such utilization, access, and availability over time.
The term "utilization" refers to a measure of the quality and safety of the available food that can be used and metabolized by the body. On a global scale, food insecurity faces a multi-headed challenge, including socioeconomic inequality, dwindling natural resources, economic instability, increasingly extreme weather patterns, and armed conflicts all contribute to this complex issue.
Local factors, including unemployment, low household income, limited social support systems, population growth exceeding resource availability, weak social networks, and a lack of education, further exacerbate food insecurity within communities. Thus, food security remains the biggest challenge for the global community as the implementation of numerous policies and programs at the international and national levels has failed to effectively address the issue.
The Need for AI
In recent years, AI and open-access data have been increasingly being used to develop strategies to predict food security and develop strategies to address it. The pursuit of a sustainable food system has driven the adoption of novel technologies like AI to improve food security.
Different agencies like FAO have emphasized the increased need for higher food production to meet the rising grain consumption requirements considering the global population growth. However, effectively addressing this challenge by only snowballing the breeding programs or increasing the production land is difficult.
Rather, a holistic approach is required for an integrated management of agriculture production. AI's potential in agriculture extends to genomics, predictive analytics, phenomics, virtual assistance, plant breeding, and integration with blockchain technology.
Predictive analytics assists in productivity enhancement, risk management, and precision farming. Similarly, in phenomics and genomics, AI facilitates tasks like cultivar selection and tissue-specific gene expression prediction. AI-powered chatbots provide virtual assistance, and decision support systems excel at data-driven decision-making. By integrating AI with blockchain technology, data management and traceability could be improved to address supply chain management challenges.
Additionally, AI plays a supplementary role in crop insurance, mechanical pollination, agricultural remote sensing, farm robotics, forestry and livestock management, and weed identification, contributing to higher agricultural production and ensuring the physical availability of food supply to vulnerable populations. Thus, the deployment of AI-enabled agriculture could meet essential nutritional needs. Such deployment can be performed through a two-pronged approach.
The first approach involves extensively using AI in developed nations to boost productivity and export surplus produce to developing countries/collaboration between wealthier nations utilizing AI to improve their agriculture, while the second approach involves combining both strategies in the approach. Agricultural productivity is anticipated to increase substantially in the coming decades using AI.
AI Applications
Food security indicators that have been analyzed using AI-based models in various studies include accessibility, availability, utilization, and affordability. Regarding the accessibility indicator, AI models have been used to evaluate food distribution disparities among households.
Specifically, the models assessed how households adopt coping strategies like the selling of livestock or rural migration and depend on social safety programs to improve their food security. Under the affordability indicator, AI models were used to explore the impact of subsidy policies on rural households' food security.
The models were utilized to assess conditions like increased water costs, cash transfer programs, reductions in agricultural subsidies, and the taxation of groundwater extraction and their effects on food security in rural regions. Additionally, the models were used to investigate the impact of food prices on household income and food security, specifically in regions with acute food insecurity like sub-Saharan Africa.
A machine learning (ML) algorithm-based AI model was also employed to determine the impact of food riots, prices, and scarcity, and political instability on food security. AI models used to evaluate the utilization of food primarily focused on the consumption patterns of particular food products like plant-based meat alternatives and dairy and social influence.
Models were utilized to investigate the food utilization of various farming styles and their impact on rural health and hunger, and the nutritional status of different groups, specifically rural farm households. The issues identified under the availability indicator using the AI models include the application of digital technologies, the energy-water-food nexus, supply/value chain, infrastructure, local knowledge, cooperatives, finance, migratory household patterns, and labor and gender issues.
Regarding the availability indicator, AI models have been used to:
- Examine the effect of climate change on pest and disease patterns, vulnerability of crops, soil health, and crop production.
- Predict changes in land use management, land use, and arable land cover under climate change.
- Determine the effects of poverty dynamics, population growth, and soil fertility decline on food availability and the application of seasonal/climate forecasts for food crop production.
- Predict crop yield and determine agricultural productivity, risks, and the application of inputs like crop variety selection, sowing dates, cropping patterns, fertilizers, and cultural practices such as mulching and cropping patterns.
- Evaluate water availability, including freshwater and groundwater resources, and irrigation management and dynamics.
- Assess the contribution of fisheries, aquaculture, and livestock to food availability.
- Investigate the potential of agricultural production systems like organic farming, industrial crops, agroforestry, agroecology, climate-smart agriculture, and smallholder farming systems.
- Evaluate farmers' decision-making and behavior regarding climate adaptation measures, input applications, and cropping patterns.
AI Models for Food Security
Artificial neural network (ANN) models have been utilized for intelligent irrigation and soil moisture detection to ensure optimal and precise watering of crops. ANN-based models have also been implemented for crop irrigation monitoring to achieve efficient water consumption.
In disease detection, agriculture expert systems using ANN and computer vision and image-based diagnostics using AI can be employed for the timely identification of diseases with high accuracy. Similarly, in genomics and plant breeding, ML models have been used for gene expression and pattern identification.
Crop trait classification has been performed using convolutional neural networks. Natural language conversation algorithms are used in AI-powered chatbots and virtual assistants for disease identification and agronomic inquiries. By combining edge computing and AI, real-time weed mapping, crop monitoring, and pine wilt disease detection can be performed.
An agriculture expert system, Agpest, has been developed for rice and wheat crop pest management using a computer vision system, genetic algorithm, and ANN. ResNet-50 attained a 95.61% accuracy in plant disease and pest detection.
A genetic algorithm along with a hybrid generalized regression neural network was devised successfully for optimizing and predicting nonlinear, difficult, and complex in vitro adventitious rooting of Bluecrown Passionflower.
Additionally, deep neural networks and ensemble-bagging algorithms were utilized to predict the biomass and yield of soybeans. Moreover, ML algorithms like boosted regression trees, random forest, and maximum entropy algorithms were employed to analyze diverse aspects related to food security.
Despite the manifold advantages, the use of AI has multiple challenges, including the need for extensive datasets and computational resources, dataset requirements, the complexity of AI algorithms, the need for continuous ML model training to reduce the possibility of errors, limited integration of blockchain with IoT and AI in agriculture, and constraints in scalability and processing power.
Overall, AI emerges as a game-changer in tackling global food security, empowering us to optimize resource allocation, predict food production, and revolutionize agricultural practices. However, broader adoption can be ensured by overcoming the existing limitations.
References and Further Readings
Sarku, R., Clemen, U. A., Clemen, T. (2023). The Application of Artificial Intelligence Models for Food Security: A Review. Agriculture, 13(10), 2037. https://doi.org/10.3390/agriculture13102037
Ahmad, A. et al. (2024). AI can empower agriculture for global food security: challenges and prospects in developing nations. Frontiers in Artificial Intelligence, 7, 1328530. https://doi.org/10.3389/frai.2024.1328530
Azizi, J. (2024). The Prospect of Food Security with Artificial Intelligence. SSRN. https://dx.doi.org/10.2139/ssrn.4825112
Siddiqi, Y., Hung, S, K-c. (2024) Cultivating a Greener Future: How AI Can Transform Agriculture for Food Security and Sustainability [Online] Available at https://blogs.adb.org/blog/cultivating-greener-future-how-ai-can-transform-agriculture-food-security-and-sustainability (Accessed on 24 June 2024)