How AI Empowers Smart Agriculture Industry?
Is Smart Agriculture a Gimmick or the Future?
According to the prediction of international research, the market scale of global smart agriculture will increase from US $9.02 billion in 2016 to nearly US $70 billion in 2025.
The huge market space has attracted the attention of many Internet giants such as Chinese domestic BAT(Baidu, Alibaba, Tencent).
At the 2016 World Conference in Baidu, Robin LI said the Internet is about to open the next stage of AI.
In the future, Baidu will launch the “Baidu brain”, which will be applied in natural language processing, speech recognition, image recognition processing, user portrait, and other fields. Especially AI + agriculture.
After that, Baidu cloud and SINOCHEM agriculture joined hands to build an intelligent agricultural production management platform based on IoT, cloud computing, artificial intelligence, and big data.
On April 10, 2018, Baidu signed a strategic cooperation agreement with Lovol heavy industry, making the great integration of AI and agricultural machinery.
From the perspective of layout, Baidu cuts into agricultural B-end service and industrial chain optimization, taking out its own cloud computing, big data platform, AI, and other technologies to cooperate with agricultural enterprises, so as to make the production of agricultural enterprises more intelligent.
On June 7, 2018, the Shanghai summit of Yunqi conference officially released Alibaba Cloud ET Agricultural Brain, which deeply combines AI with agriculture.
At present, Alibaba cloud ET agricultural brain has been applied to pig breeding, apple, and melon planting, and has the functions of digital file generation, full life cycle management, intelligent agricultural analysis, full link traceability, etc.
More read: Facial Recognition Labeling Case in Agriculture
Ali has launched rural Taobao projects in rural areas. By transforming the local industrial supply chain system, it has gradually explored an S2B e-commerce（Supply chain platform To business） mode for agricultural products. It has opened up the local supply chain, connected with businesses on Tmall and other platforms, and then sold to consumers.
In April last year, Tencent cloud signed a strategic cooperation agreement with the local agricultural group, supply chain technology company, industry, and finance fund to jointly build a “smart agriculture platform”.
Is Smart Agriculture a Gimmick or the Future?
At the highest stage of modern agricultural development, smart agriculture mode mainly applies new technologies such as big data, cloud computing, Internet of things, artificial intelligence, etc., and is applied to agricultural production, supply, sales, circulation, and other stages. Through the big data platform, the type and quantity of data collection will be greatly increased. Through the Agricultural IoT, we can observe the current growth environment of crops and animals. Through artificial intelligence technology, we can take advantage of the human workforce for more efficient and accurate agricultural work.
Therefore, we believe that through the use of emerging technologies, the smart agriculture model will bring huge development space for agricultural information services, improve the efficiency of information collection, enrich the quantity and type of data, accelerate the speed of information analysis, and realize the early prediction of disaster.
The effect of smart agriculture on agricultural plant protection services mainly includes: artificial intelligence technology gradually replaces human resources, carries out more efficient and accurate protection work, and saves human capital. Through the cheap third-party service platform of big data and cloud computing, the information of plant protection can be better transferred, transmitted, and shared by mobile phone, so as to improve work efficiency.
Why the High-Quality Training Data is so Important to AI Machine Learning?
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 the process of in-depth industrial landing, there is still a gap between artificial intelligence technology and enterprise needs. The core goal of enterprise users is to use artificial intelligence technology to achieve business growth. Actually, artificial intelligence technology itself cannot directly solve all the business needs. It needs to create products and services that can be implemented on a large scale based on specific business scenarios and goals.
If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain model’s commercialization, they must gradually move forward from the general dataset to create their own unique one.
Common Labeling Types:
- Object Recognition
- Crops Monitoring And Weeds Control
- Pest & Disease Diagnosis
- Fruits Growth Dectection&Live Stock Monitoring
- Object Classification
- Farm Automation
- Agricultural Robot Crop Harvesting
- Auto Pesticide Spray
- Soil Management
- Fruits Picking
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