AI and agriculture

Introduction

As technology improves day by day, so is the human population and our food needs. Traditional agriculture technologies are no longer efficient enough for us to get food. As a result, we push ourselves to look into different things and explore new fields, and then we finally found Artificial Intelligence.

With Artificial Intelligence, many new logic and methods were invented, such as:

• Artificial neural networks
• Automation and wireless systems networks
• Implementation of fuzzy logic system

Each method is better at handling different workloads, it enhances the speed at which problems can be solved. So it is important to understand what the problem is, then choose the right logic to solve with the least effort.

Artificial neural networks(ANN)

Compare to traditional systems, ANN has many advantages. One main benefit of ANN would be the ability to predict and forecast with the data they have stored. Since it is not hard programmed, ANN can be trained by feeding the system more and more database. The larger the database, the smarter the logic would get. This would be the best solution for crop disease infestation and Pestidicde control because it can always predict how many treatments is needed for the infestations. Also how many pesticides is needed to keep the crops healthy. Even if a mistake was made, ANN is still trainable and it would only get better.

Automation and wireless systems networks

This method collects information with smart sensors. They can be wind sensors, temperature sensors, or air humidity sensors. After the system collected enough information, it would go through a set of calculations and come up with the best solution. It will be able to predict things before they actually happen. Although this method can be better because this one is more cost-effective.

Implementation of fuzzy logic system.

The implementation of fuzzy logic system is used to recognize, determine, and identify objects. Base on the object, the system will be able to decide to optimize. For example, in the article, this system was used to detect whether the land was good for planting by examining the soil’s textures, slope, and color. This one is more precious compare to the previous one because this uses evidence to make decisions. While the other two make the closest assumption according to pieces of information they collected.

Conclusion

With the different types of methods, we can see how far AI has already gone. By implanting AI technologies into agriculture, efficiency and reward would increase while labor and cost would reduce.

Work Cited:

Artificial intelligence in agriculture. (2019). KeAi.

(n.d.). Retrieved November 29, 2020, from https://search.lib.buffalo.edu/permalink/01SUNY_BUF/1rt4c9d/alma9938855671004803