This blog post explores the changes and possibilities AI will bring to agriculture, focusing on real-world application cases.

The Future of Artificial Intelligence After AlphaGo

Back in March 2016, the artificial intelligence (AI) program AlphaGo defeated Lee Sedol in a Go match with a score of 4 wins and 1 loss. This victory sparked an explosive global surge in interest regarding the potential and power of artificial intelligence. This event sparked vigorous discussions about how AI’s role could extend beyond mere computational power or performing specific tasks, potentially reaching fields requiring uniquely human judgment and intuition. Subsequently, experts and futurists began predicting the changes AI would bring to various industries. Many simultaneously expressed both warnings and expectations about AI’s potential to replace numerous job categories.

Agriculture and Artificial Intelligence: The Beginning of Change

Among these, agriculture is drawing particular attention as a field where AI’s potential impact is especially significant. Traditionally, agriculture has long been centered on labor performed directly by people. Even after mechanization progressed, it remained highly sensitive to weather and environmental changes. However, if AI is fully integrated into agriculture, it is expected that problems previously difficult to solve with manual labor can be handled more efficiently. For instance, AI can monitor crop conditions in real time to predict pest and disease outbreaks or calculate optimal amounts of fertilizer and water. This holds significant potential to boost agricultural productivity while minimizing environmental impact.

Specific AI Applications and Expected Benefits

Managing crop pests and diseases poses a major challenge for farmers. While swift action is essential when infestations occur, accurately identifying the specific pest or disease often takes considerable time. Recently, deep learning-based AI systems trained on images of pest and disease symptoms have been developed, enabling rapid and precise diagnosis. This plays a crucial role in quickly diagnosing affected crops and implementing necessary measures immediately. Through such systems, farmers can resolve pest and disease issues in time, reducing damage and maintaining yields.
Another critical challenge in agriculture is weed management. Traditionally, farmers have relied on spraying pesticides or manual removal, which not only requires significant labor but also burdens the environment. However, robots equipped with artificial intelligence enable precise operations capable of selectively removing weeds. This contributes to reducing unnecessary pesticide use and enhancing agricultural sustainability. For instance, Blue River Technology has developed an AI-powered robot that targets and removes specific weeds, simultaneously achieving the dual goals of labor savings and environmental protection.

Smart Agricultural Systems Reducing Climate and Environmental Impacts

Agriculture is heavily influenced by climate and weather, leading to frequent losses due to environmental changes. For instance, heavy rains causing flooding or droughts scorching crops can severely impact yields. To address this, artificial intelligence is being utilized to build systems that monitor climate and environmental conditions in real-time through sensors and big data analysis, automatically suggesting or executing necessary measures. For instance, in greenhouse cultivation systems, technology is being developed where AI sets optimal temperature, humidity, and lighting conditions to maintain the best environment for crop growth. A Japanese company has successfully grown cabbage using such a smart agriculture system, contributing to consistent crop quality and increased yields.

Labor Replacement and Productivity Enhancement Through Artificial Intelligence

The labor shortage in the agricultural sector is a serious global issue. Particularly in rural areas facing aging populations and a lack of young workers, artificial intelligence is gaining attention as a solution to address labor shortages. For example, Japanese computer systems developer Makoto Koike created a system using deep learning to automatically grade cucumbers. This significantly reduced work time while enabling precise quality control. Such automated grading systems reduce simple tasks, saving labor and contributing to increased agricultural productivity and efficiency.

Conclusion: The New Future of Agriculture Unlocked by Artificial Intelligence

In conclusion, the introduction of artificial intelligence into agriculture is expected to bring about several positive changes. AI can play a crucial role in performing tasks difficult for humans, reducing environmental damage to agriculture, and alleviating labor shortages. Of course, these technologies are not yet fully developed; current accuracy in identifying pests and diseases is only around 95%, and the types of pests and diseases that can be distinguished are limited. However, as artificial intelligence technology advances further and these limitations are overcome, it will bring significant innovation to the agricultural sector. Particularly as smart agriculture, utilizing cutting-edge technology, gradually expands, agriculture has great potential to evolve beyond a simple primary industry into a future-oriented industry.
Future agriculture is advancing in an efficient and sustainable direction based on artificial intelligence, which will ultimately contribute to solving food issues and protecting the environment.