Integrating AI and Human Expertise for Sustainable Agriculture and Forestry:
The global shift towards digital transformation is largely driven by advances in AI, particularly statistical ML. AI’s capacity for intelligent analysis, modeling, and management is becoming crucial in sectors like agriculture and forestry, where it aids in the sustainable use and protection of natural resources. These advancements rely on cyber-physical systems supported by big data and computational power, enabling tasks such as radiology interpretation to surpass human performance. However, the challenge lies in integrating and explaining multimodal data from various sources, such as sensors and images. AI models are often sensitive to small changes, necessitating a focus on trustworthy AI that emphasizes explainability and robustness. Human-centered AI (HCAI) emerges as a solution, blending human expertise with AI to enhance decision-making without replacing human roles.
HCAI advocates for a human-in-the-loop approach, leveraging expert knowledge to improve AI’s performance and robustness. This approach is crucial in agriculture and forestry, where complex, real-world tasks benefit from human conceptual understanding. Integrating AI and human expertise addresses the need for reliable, explainable AI systems while ensuring that technology complements rather than replaces human capabilities. As AI technologies advance, focusing on intelligent information fusion, robotics, and robust decision support systems will be essential for achieving sustainable and efficient agricultural and forestry practices.
Classification of AI Technologies:
AI technologies are categorized by their autonomy: Autonomous AI Systems operate independently, such as self-driving cars and drones. Automated AI Systems handle repetitive tasks within specific domains, like robotic process automation and forest management. Assisted AI Systems support humans in routine tasks, improving efficiency and quality, as seen in weather forecasting and ambient assisted living. Augmenting AI Systems enhances human intelligence through machine integration, such as augmented reality applications. In agriculture and forestry, advancements include mobile apps, smart sensors, drones, and IoT tools, which optimize practices by processing real-time data on various environmental factors.
Future AI Research Areas in Agriculture and Forestry:
Future AI advancements in agriculture and forestry focus on three key areas: intelligent sensor information fusion, robotics, and embodied intelligence. These developments require human-centered design across three generations: Generation 1 involves applying existing technology; Generation 2 adapts existing technology for medium-term improvements; and Generation 3 seeks to advance beyond current state-of-the-art solutions. Key challenges include enhancing sensor reliability, improving real-time data fusion, and developing robots with effective human interaction and explainable AI (XAI) capabilities. Addressing these challenges promises significant benefits for agricultural and forestry practices.
Human-Centered AI: Integrating Expertise in Agriculture and Forestry:
Human-centered AI integrates human expertise with machine learning to enhance real-world applications. In agriculture, they are combining human input with AI to address data uncertainties and computational challenges, improving decision-making and sustainability through sensors and robotics. In forestry, it means incorporating human expertise into autonomous systems to optimize timber harvesting and maintenance. Challenges include creating user-friendly tools for inexperienced farmers, integrating diverse data sources, and developing autonomous forest machines. Both fields aim to align technology with human values and improve efficiency and sustainability.
Conclusions on Human-Centered AI in Agriculture and Forestry:
Advanced technologies are now affordable and widely used, but integrating intelligence into these tools is the next step. Human-centered AI plays a crucial role by combining domain experts’ experiential knowledge with artificial intelligence. The study highlighted the importance of agriculture and forestry, defined key AI concepts, and explored state-of-the-art autonomous and augmented systems in these fields. It identified three key research areas for the next seven years: intelligent sensor information fusion, robotics and embodied intelligence, and augmentation and verification. The study concludes that these areas will drive significant progress in agriculture and forestry.
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