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AI Technology to Help Agriculture, the Future Can Be Expected — Part 3

AI, machine learning (ML), and IoT sensors can provide algorithms with rich real-time data to improve agricultural productivity and crop yields, and reduce food production costs. According to UN projections on population and starvation, the global population will increase by a further 2 billion by 2050, and agricultural productivity will need to grow by 60% to provide adequate food. According to data released by the U.S. Department of Agriculture’s Bureau of Economic Research, in the United States alone, the total market for growing, processing, and food distribution businesses is as high as $1.7 trillion. By 2050, artificial intelligence and machine learning are likely to be at the heart of new technologies. It will help us cope with the expected food needs of an additional 2 billion people.

Thirdly, yield mapping is an agricultural technique that brings immeasurable value to crop production planning by supervised machine learning algorithms to find patterns in large-scale datasets and understand the orthogonality between different patterns in real-time.

Today, we are able to roughly judge the potential yield of a given field before the planting cycle begins. By combining machine learning techniques with 3D mapping, sensor data, and field color data from drones, agricultural experts can quickly predict the yield of a given crop under potential soil conditions. These datasets captured by drones are accurate and reliable. With the help of supervised and unsupervised machine learning algorithms, agricultural experts can determine how to maximize field yields.

Fourthly, the United Nations, international agencies, and large agricultural projects are combining drone data with on-site sensors to bolster pest management. By combining thermal camera data from drones with sensors that monitor the relative health of plants, agricultural management teams can predict and identify pests before they occur with the help of AI. The UN is currently working with PwC to assess potential pest infestations in palm plantations across Asia.

Experts combine field sensors with drone data to fine-tune machine-learning algorithms, so as to help farmers get higher yields.

Fifthly, today’s severe shortage of agricultural workers makes AI and machine learning-based smart tractors, agricultural robots, and other smart machines the first choice for farming in remote areas.

Currently, large agribusinesses can’t find enough employees and they can only rely on robotics to harvest crops from hundreds of acres, which also plays a positive role in remote area security. By programming autonomous robotic devices, they can spread fertilizers to crops, thereby lowering operating costs and further boosting field yields. Agricultural robots is getting more sophisticated rapidly.

Agricultural robotics has proven to be able to quickly capture valuable data that can be used to fine-tune AI and machine learning algorithms to further raise crop yields.

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.


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