The impact of Technology on various sectors of the economy cannot be understated.
Certain factors such as unprecedented surges in population growth and global warming have caused a gap in supply that is getting increasingly difficult to manage.
The advent of Artificial intelligence makes it easier to manage this gap. For instance, drones have been used to map large expanses of land and gather relevant information more quickly and more accurately than any human could ever manage.
In this article, we will be looking at a few AI’s in agriculture today and the role of data annotation in their development.
What is Data Annotation?
Data annotation is the process of categorizing and labeling information for machines to understand. If an AI was a growing child, Data Annotation is the way to feed it.
There are various types of Data Annotation such as Image annotation, Video annotation, Text annotation, Audio annotation, and LiDAR.
What Is The Future of AI in Agriculture
Artificial intelligence has positively impacted various lives in various capacities. In the world today, you find AI in several sectors such as Medicine, Law Enforcement, and Transportation. Agriculture is no exception.
AI has already helped the sector flourish in various ways, some of which are:
Crop and Soil Monitoring
Researchers employ the use of data annotations to train the machine learning algorithm to first recognise that a certain plant is damaged, and secondly identify the reason.
The Use of Robotics in Automation Processes
Image annotation allows robots to carry out manual tasks that are repetitive and very time-consuming. This includes tasks such as weeding, harvesting and planting crops etc.
Gathering Data
Robots such as TerraSentia, peruse through farmland and scrutinize the current state of crops, then employ data annotations such as LiDAR to help them move around and gather valuable information about plant health and physiology.
Sorting Fruits and Vegetables
After fruits and vegetables are collected, There are sorting tasks by robots to separate the rotten and healthy fruits/vegetables before delivering them. These robots help detect any problem wrong with the crops and use the information to predict which items will last longer for shipment and which items are suitable for local consumption.
Crop Yield Forecasting
AI uses learning datasets such as text annotations, to forecast the crop yield through portable devices like tablets and smartphones, consequently ensuring a better farming experience.
Conclusion
Despite the Important roles that AIs play in Agriculture, there are several roles they can still play.
There are several gaps in supply due to inefficient transportation and storage facilities that AIs still have to fill.
While Machine automation may be frowned upon in some circles due to fears that it replaces humans, There is no doubt that it is the way forward. Therefore we must improve our existing processes and innovate new ones.
