AgriTech: How Data Labeling and AI Technologies are used in the Evolution of Farming
-Written by Mananya Senona, Gabriella Lupini & Edited by Ridha Moosa for Enlabeler
Data labeling and AI technologies have hugely impacted the development of the agricultural industry. It, therefore, comes as no surprise that data labeling and AI technologies are at the center of modern-day advancements in contemporary farming. Its impact has been so vast in influencing the agricultural industry, creating its own industry called “AgriTech”.
What is AgriTech ?
The AgriTech industry explores how agriculture can be made more efficient in its outputs. AgriTech is about merging traditional farming techniques with sophisticated technological innovations in order to improve the output of agriculture and its growing process. The advantages in agricultural technology allow for better optimisation, new automation, more information to be extracted, and smarter ways of farming; all this to improve the yield of farms to provide communities with sustenance.
Some use cases of innovations in Agritech may be using robots and automated systems to aid farmers when picking or planting crops, or using big data and machine learning to better understand how to improve soil fertility. The application of AI in agriculture is revolutionary because it employs smart intelligence and complex algorithms in conjunction with human intelligence of reasoning, perception, and physical activity into a machine that can easily execute these tasks in an even more complex and efficient manner.
How Data Labeling Is Applied In AgriTech
With the advancements in AI and agriculture, farmers are now able to leverage new data-driven solutions in order to improve crop yields and drive business value. The use of data analytics in agriculture enhances its effectiveness. Data labeling in AgriTech helps provide the machine learning algorithms to learn from an expected output or “label”. The following are examples of how data labeling is applied in Agri-Tech:
- Orchard Boundary Mapping
Orchard boundary mapping in farming is a process done by using data analytics to understand and map out boundaries and routes in the field. Orchard boundary mapping uses mobile robot localisation in orchards that rely on precise orchard maps in order to efficiently estimate its position and orientation while moving between tree rows. The boundary placement style places labels around the polygon, within its perimeter, usually matching labels of adjoining polygons.
2. Behavioral Tracking
Behavioral tracking is done by detecting key points that when combined indicate a specific behavioral stance. Adding these key points creates context to analyse imagery, for example in livestock farming and detecting behavior patterns.
3. Anomaly Detection
Anomaly detection in Agritech allows farmers to detect the less occurring phenomena in their operations; data points or patterns in data can be collected to ascertain if there are anomalies that do not correspond to normal behavior. Using anomaly detection is a crucial approach for detecting significant occurrences on the farms. This is a process of identifying observations that deviate from the norm. Anomalies can be classified as point anomalies, contextual anomalies, or collective anomalies. These classifications help farmers detect defects, such as bug infestations or rotting fruits.
4. Yield Estimation
Yield estimation prediction is an important factor in agricultural farming as farmers need information regarding crop yield before partaking in farming duties in the field. Estimating the crop yield can be achieved by using two methods, namely the crop growth model and the data-driven model. The crop growth model involves utilizing mathematical models to represent the impact of every factor affecting crop yield. The data-driven model uses yield prediction models that measure crops directly from the field.
Conclusion
The introduction of Agritech in revolutionizing traditional farming has raised concerns about the replacement of humans working in agriculture, who have primarily relied on the farming industry for living and survival purposes. In contrast, the stats of our growing global population, which is predicted to reach more than nine billion by 2050, indicates it would require more food production in order to fulfill the growing population.
Smart agriculture technologies are thus effective instruments for increasing farm sustainability and production as they generate many spatial, temporal, and time-series data streams that, when analysed, can reveal several issues regarding on-farm productivity and efficiency. We can therefore argue that the technology is needed in order to supplement the rising increase demand for land, food, and water resources.
