In some municipalities in Hokkaido to be designated as national strategic special zones, autonomous tractors will be allowed to travel on public roads without a driver onboard. The government decided Thursday to designate 19 municipalities in the Tokachi region of Hokkaido, including the city of Obihiro, as national strategic special zones to promote artificial intelligence-powered agriculture. The government will apply special deregulation measures to the areas, including simplifying the procedures needed to operate robot tractors autonomously on public roads and spraying pesticides with drones. Under current laws, robot tractors capable of autonomous operation must have a driver when traveling on public roads, such as from a warehouse to a field. When multiple tractors are used on vast farmland, each tractor must be crewed, thus preventing farmers from fully realizing the benefits of the labor-saving technology.

Companies are turning to artificial intelligence and sensor technology to improve the efficiency of indoor farming in Japan as the country faces a growing shortage of farmers. Oishii Farm, which runs a strawberry factory in the United States, recently established a new research and development base in the city of Hamura in western Tokyo. It plans to develop efficient methods for cultivating other crops, such as tomatoes, indoors and robots capable of automatically planting and packaging produce. The company uses AI to manage the breeding environment for bees, which are essential for pollination. The technology has raised the pollination rate for its strawberries to 95%. "We're able to achieve hundreds of years' worth of traditional outdoor breeding progress within a single year," said Hiroki Koga, co-founder and CEO of Oishii Farm, emphasizing the benefits of plant factories unaffected by seasons or weather.

Startups and Big Ag are using Crispr gene editing to create crops that taste better and grow on a hotter planet. But will they find a market? If you wanted to create better blackberries, you could try to breed a variety without the seeds that get stuck in your teeth. Or one without the thorns that make the berries hard to pick. Or you could aim for more environmentally efficient plants that produce more berries per acre with less water and fewer chemicals.

Agriculture is ready for AI, but its data isn't Data accuracy, structure, and governance are foundational components required for agricultural AI. Artificial intelligence is transforming what is possible in agriculture, but industry leaders should be wary of investing in AI without first laying the groundwork. The use cases are promising, especially for an industry navigating volatile fertilizer costs, unpredictable weather, and margins that leave little room for error. Research shows AI-enabled predictive models can improve crop yield by 26%, reduce water use by 41%, and cut chemical usage by 33%. However, what AI vendors usually won't tell you is that these solutions are only effective if you have a clean, solid data foundation. However, at Reltio, we have experience in this area, including leading technology strategy at a major agricultural distributor and building a data platform used by enterprises worldwide-we've seen it first hand.

To meet this moment, we need YOU. For five decades, has been exposing the corruption that the powerful would rather keep buried. That fight for the truth is at a pivotal point, and it takes readers like you to make it possible. To meet this moment, we need YOU. That fight for the truth is at a pivotal point, and it takes readers like you to make it possible. Amid public outcry over water-guzzling server farms, a Guardian analysis indicates trouble ahead.

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We also gathered milk yield data and outdoor weather conditions.

Large Language Models (LLMs) have demonstrated significant potential in democratizing access to information. However, in the domain of agriculture, general-purpose models frequently suffer from contextual hallucination, which provides non-factual advice or answers are scientifically sound in one region but disastrous in another due to variations in soil, climate, and local regulations. We introduce AgriRegion, a Retrieval-Augmented Generation (RAG) framework designed specifically for high-fidelity, region-aware agricultural advisory. Unlike standard RAG approaches that rely solely on semantic similarity, AgriRegion incorporates a geospatial metadata injection layer and a region-prioritized re-ranking mechanism. By restricting the knowledge base to verified local agricultural extension services and enforcing geo-spatial constraints during retrieval, AgriRegion ensures that the advice regarding planting schedules, pest control, and fertilization is locally accurate. We create a novel benchmark dataset, AgriRegion-Eval, which comprises 160 domain-specific questions across 12 agricultural subfields. Experiments demonstrate that AgriRegion reduces hallucinations by 10-20% compared to state-of-the-art LLMs systems and significantly improves trust scores according to a comprehensive evaluation.