You Still Miss QQ Farm Stealing Vegetables, AI Technology Has Already Begun To Grow Vegetables
Many Chinese people who now are in their 30s and late 20s have been crazy for the online game QQ Farm, setting alarms to steal vegetables, and even reading various strategies and posts late at night to improve the yield of the farm.
However, when many people still miss those crazy days for QQ Farm, some people have already used QQ Farm-like technology in modern farms for agricultural production and life. That is “AI Farm” In fact, the word “AI farm” is said by the editor to make it easier for everyone to understand. In layman’s terms, it is to use artificial intelligence technology to monitor environmental data, crop production conditions, market demand, etc. in the early, middle, and even later stages of agricultural production, and to produce, plant, and sell crops.
The outstanding performance of AI technology in agricultural production
AI technology monitors the air humidity and plant water demand in the control area and sets a reasonable amount of irrigation water. According to the actual situation, automatic irrigation, regular humidification, periodic irrigation, and other modes are adopted to ensure the water demand of crops and save irrigation water. They also ensure the water needed for the growth of crops and avoid the occurrence of droughts and floods caused by insufficient or excessive irrigation water during the growth of crops.
Although soilless cultivation techniques are used in many agricultural productions, the soil is still the “home field” of crop production to a large extent. AI technology detects and analyzes soil composition, selects suitable crop varieties for planting, and uses effective fertilizers to save resources, improve taste, and increase yield. At the same time, AI technology helps people get a reasonable time and place for fertilization by analyzing the soil, thereby achieving high productivity and saving costs.
The quality of seeds directly affects the growth of crops. The AI technology detects seed images and neural networks, without destroying the seed structure. With fast detection speed and high accuracy, it plays a significant role in improving the yield and quality of agricultural products.
Under the statistics of big data, AI can also predict the problems that may occur in the growth process of crops, and provide corresponding solutions based on the statistics of crop growth environment data and growth status data. Through the detection and automatic perception of factors such as temperature, humidity, carbon dioxide content, moisture, and other factors in the growth environment of crops, timely adjustments are taken, and measures such as temperature control, shading, and irrigation are adopted to ensure the effective growth of crops. AI technology can also achieve the goal of intelligent prevention, management of pests and weeds, and reduce economic losses.
Just like the operation of growing vegetables with a mouse click in QQ Farm, AI technology has been putting into practice a lot.
In the first and second International Smart Greenhouse Planting Challenge, AI technology was used to successfully plant cucumbers and tomatoes. By the way, the 3rd International Smart Greenhouse Planting Challenge is underway. AI technology will be used to grow lettuce. This is one of the must-haves for barbecue. Maybe in the near future, the lettuce on our barbecue table will be all grown by AI technology.
With the increasing pressure on the world’s population and the increasingly serious pollution of land and the environment, the application of AI technology in agricultural production has become the consensus. However, the combination of AI technology with traditional agriculture also faces many challenges and problems. How to predict and overcome these difficulties will be a problem we face together, and we need to explore various possibilities of AI technology together.
The demand for data labeling continues to increase
At present, the research community is doing unsupervised, small-sample deep learning work. Through three-dimensional synthetic data, the machine is trained with synthetic data, so as to minimize the data collection and labeling process. In this way, the machine can learn and evolve independently. However, as there is a lack of theoretical technology breakthroughs, although the technology is growing fast, the overall level is still relatively low. The current deep learning still relies on the big data model based on statistical significance, which requires scalable data.
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