AI Technology to Help Agriculture, the Future Can Be Expected — Part 4
Occupying a fundamental position in the three industries, agriculture, as the foundation of human survival, is crucial to economic and social stability and development. However, with the rapid population growth, the gradual shrinking of arable land and the acceleration of urbanization, the challenges facing agriculture are becoming more and more severe. In order to cope with this challenge, people both at home and abroad are exploring to improve the quality and efficiency of agriculture through information technology. In this context, the new model of smart agriculture based on artificial intelligence has developed swiftly, and many typical cases have emerged, providing a useful reference for the application of AI in agriculture.
For instance, determine the total output according to the crop yield rate, thereby formulating a reasonable and effective crop pricing strategy.
Accurately grasping the yield and quality levels of crops can help agribusinesses, cooperatives, and farmers better develop pricing strategies. Considering that the overall market demand for a specific crop is basically constant, all parties can choose strategies such as fixed selling price, unified selling price, or even flexible selling price according to the harvest of the crops. The data alone can save millions of dollars each year.
Another application: monitoring and maintaining livestock health — including vital signs, daily activity, and food intake — has become a new frontier for AI and machine learning.
To ensure good care for livestock over the long term, we must keep track of how they actually respond to the current diet and housing conditions. Agricultural experts can understand what factors determine the mood of the cows and make appropriate adjustments to improve milk production thanks to AI technology. For the livestock industry with cattle and other livestock, the introduction of new technologies has led to unprecedented new directions for ranchers to fatten profit margins.
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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