Nerd For Tech
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Nerd For Tech

AI Technology to Help Agriculture, the Future Can Be Expected — Part 1

Smart Agriculture is Booming

Global spending on connected smart farming technologies and systems, including artificial intelligence and machine learning, is expected to triple to $15.3 billion by 2025, according to a forecast published by BI Intelligence Research.

According to data released by Markets & Markets, spending on AI technologies and solutions in agriculture alone is expected to grow from $1 billion in 2020 to $4 billion in 2026, with a compound annual growth rate (CAGR) of 25.5%.

According to PwC, IOT-based Agriculture (IoTAg) monitoring has become the fastest growing technology area in connected smart agriculture, with the total market projected to grow to $4.5 billion by 2025.

Technical Difficulties of AI+ Agricultural Application

Throughout the link before, during, and after agricultural production, there are completely different application scenarios and professional directions. For example, upstream pre-production focuses on biotechnology, oriented to informatization cultivation, biological breeding, pesticides, and fertilizers. Mid-stream production focuses on digital agriculture, oriented to agricultural big data, farmland monitoring, and agricultural drones. And downstream post-production focuses on quality inspection in consumption level, agricultural product processing, cold chain logistics, etc.

In the long-term follow-up reports and interviews on AI + agriculture, the technical difficulties of the insiders are nothing more than the following two points:

1. It is difficult to collect agricultural data.

2. Each agricultural customer has different scenarios and requires customized training. At the same time, agricultural customers and R&D are required to jointly define the corresponding industry standards.

Of course, this is just a general overview of the challenges in most smart agriculture applications. In the actual business promotion, there may be problems that need to be solved urgently. Previously, in the process of business promotion, a domestic plant factory came across problems such as a shortage of agricultural talents, low efficiency of manual check of production conditions, and inability to popularize and apply on a large scale.

An AI development platform optimizes and upgrades plant factories, and conducts product research and development such as pest monitoring models and growth influencing factor models, digitizing and commercializing the experts’ experience.

Product quality and output are improved by efficient and accurate machine identification. The output of defective products is reduced, while the total output is increased by 15%, and the cost of production materials (seeds, substrates, nutrient solution) is lowered by 15%.

More Labeled Data is Needed

From the perspective of the research direction of artificial intelligence technology, whether in the field of traditional machine learning or deep learning, supervised learning based on training data is still a major model training method. Especially in the field of deep learning, more labeled data is needed to improve the effectiveness of the model.

In particular, traditional enterprises with intelligent transformation and technology enterprises need the assistance of training data service providers with rich project experience to help sort out the data labeling instruction and to obtain more suitable data. The use of high-quality data in special scenarios reduces the research and development cycle, accelerates the implementation process, and helps enterprises to make faster and better intelligent transformations.

End

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source:https://www.163.com/dy/article/GTHK2DJV0552OFB6.html

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