China’s Nanning applies AI to enhance agricultural production – FreshPlaza

Report on AI Integration in Nanning’s Agriculture and its Contribution to Sustainable Development Goals

Introduction: Advancing SDG 2 (Zero Hunger) through Technological Innovation

• Nanning, China, is integrating Artificial Intelligence (AI) into its agricultural sector to enhance food security and promote sustainable agriculture, directly aligning with the objectives of SDG 2 (Zero Hunger).
• This initiative marks a strategic shift from traditional, experience-based farming to data-driven agricultural management, optimizing the entire value chain.
• A notable outcome in the first half of the year was a 3.7% year-on-year increase in vegetable production, demonstrating progress towards achieving food security targets.

Enhancing Crop Resilience and Productivity in Line with SDG 2 and SDG 12

1. AI-Powered Pest and Disease Management:
   • A mobile application allows citrus growers to diagnose crop diseases with 95% accuracy by uploading images, facilitating rapid response to outbreaks.
   • This technology reduces crop loss and supports the sustainable use of treatments, contributing to SDG 12 (Responsible Consumption and Production).
   • The system empowers small-scale farmers by providing expert consultation, thereby increasing their productivity and income, a key target of SDG 2.
2. City-Wide Smart Monitoring Network:
   • The Nanning Plant Protection Station has established a network of 19 field monitoring stations equipped with 80 advanced devices.
   • This infrastructure provides real-time data to predict pest outbreaks, enabling preventative action and reducing reliance on broad-spectrum pesticides, which supports sustainable production patterns.

Fostering Innovation, Economic Growth, and Responsible Production (SDG 8, SDG 9, and SDG 12)

• Industry Innovation (SDG 9): The use of AI, the Internet of Things (IoT), and big data represents a significant technological upgrade for the agricultural industry, building resilient infrastructure and fostering innovation.
• Economic Growth (SDG 8): By increasing efficiency and yield, AI technology directly contributes to higher incomes and promotes sustained, inclusive economic growth for farming communities.
• Post-Harvest Processing and Waste Reduction (SDG 12): Automated grading systems sort fruit based on quality metrics such as size, sugar content, and defects. This AI-supported processing enhances product value and reduces post-harvest losses, aligning with goals for responsible consumption and production.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

• SDG 2: Zero Hunger

  The article directly addresses this goal by focusing on increasing agricultural productivity and implementing sustainable farming practices. The use of AI to manage pests and diseases helps secure crop yields, contributing to food security. The text mentions a 3.7% year-on-year increase in vegetable production, which is a direct contribution to this goal.

• SDG 8: Decent Work and Economic Growth

  The integration of technology like AI and IoT into agriculture promotes higher levels of economic productivity. The article highlights how these innovations lead to process optimization and increased output (e.g., an orchard producing 70,000 kilograms of fruit), thereby supporting economic growth in the agricultural sector.

• SDG 9: Industry, Innovation, and Infrastructure

  This goal is central to the article, which describes the development and application of advanced technologies (AI, IoT, big data) to upgrade the agricultural industry. The creation of a diagnostic app by a local technology company and the establishment of 19 monitoring stations with 80 advanced devices represent significant advancements in innovation and infrastructure.

• SDG 12: Responsible Consumption and Production

  The article touches upon this goal by describing methods to reduce food losses. Efficient pest and disease management prevents pre-harvest losses. Furthermore, the automated fruit sorting systems that grade produce by quality, size, and sugar content help optimize the supply chain and reduce post-harvest losses, ensuring that more of what is produced reaches the consumer in good condition.

2. What specific targets under those SDGs can be identified based on the article’s content?

• Target 2.3: By 2030, double the agricultural productivity and incomes of small-scale food producers.

  The article supports this target by describing how technology helps growers like Su Jianquan increase their output (70,000 kg of fruit), which directly relates to enhancing agricultural productivity.

• Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production.

  The shift from “experience-based farming toward technology-supported management systems” using AI for data-driven decision-making and risk management is a clear implementation of resilient and sustainable agricultural practices.

• Target 8.2: Achieve higher levels of economic productivity through diversification, technological upgrading and innovation.

  The adoption of AI, IoT, and automated grading systems in the farming value chain is a direct example of technological upgrading and innovation aimed at boosting productivity in the agricultural sector.

• Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries…encouraging innovation.

  The development of an AI application by Guangxi Huiyun Information Technology Co., Ltd. for disease diagnosis and the deployment of smart monitoring equipment by the Nanning Plant Protection Station are prime examples of upgrading technological capabilities and fostering innovation in agriculture.

• Target 12.3: By 2030, halve per capita global food waste at the retail and consumer levels and reduce food losses along production and supply chains, including post-harvest losses.

  The article implies a reduction in food losses through two mechanisms: 1) more efficient pest and disease management reduces pre-harvest losses, and 2) automated fruit sorting systems reduce post-harvest losses by ensuring quality control and efficient grading.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

• Indicator for Target 2.3/2.4 (Productivity): The article explicitly states that “vegetable production, including edible fungi, increased by 3.7% year on year.” It also mentions a specific grower’s output of “more than 70,000 kilograms of fruit last year.” These figures serve as direct indicators of agricultural productivity.
• Indicator for Target 9.5 (Innovation and Technology): The effectiveness of the new technology is measured by its diagnostic accuracy, which is stated to be “95% accuracy” for identifying citrus diseases. The scale of infrastructure development is indicated by the establishment of “19 national and regional field monitoring stations” equipped with “80 advanced devices.”
• Indicator for Target 12.3 (Reduced Food Loss): While not providing a direct number, the article implies a reduction in food loss. The efficiency of the AI system in helping growers “manage disease outbreaks more efficiently” suggests a reduction in pre-harvest losses. The automated sorting of mandarins by “size, color, sugar content, and surface defects” is an indicator of improved post-harvest handling to minimize waste.

4. Summary Table of SDGs, Targets, and Indicators

Source: freshplaza.com