Manasi Nachankar (India), with a background in computer engineering and founder of the Almería’s committee of the International Association of Students in Agricultural and Related Sciences (IAAS), enrolled in MIAM to specialize in Agribusiness and combine her work of specialization with her family business. In this article, she explains 3 things she learned in this master’s about Big Data. Keep reading to learn them.
Big Data has become a buzzword in recent years, and its significance cannot be overemphasized. It refers to the massive amounts of data that are generated every day and the technologies used to analyze and extract insights from that data.
In today’s world, with advancements in technology, the amount of data being generated is growing exponentially, and businesses, organizations, and governments rely heavily on Big Data to make informed decisions, gain insights, and drive innovation.
How big is Big Data?
Big Data refers to the large volume of data generated every day, every hour, and every minute. The size of Big Data is measured in terms of its volume, velocity, and variety.
In real-time Spanish agriculture, Big Data is generated by various sources, including sensors, weather forecasts, and agricultural machinery. These data sets can be vast, and sometimes challenging (check the 3 Challenges of Big Data), as farmers collect information on soil moisture, air temperature, precipitation, and other environmental factors. They also collect information on crops, such as the number of plants, their growth rate, and the quality of the soil. All this data helps farmers make informed decisions about planting, harvesting, and fertilizing their crops.
The Three V’s of Big Data
The three V’s of Big Data are volume, velocity, and variety. They help us understand the characteristics of Big Data and why it’s so challenging to manage.
- Volume: The vast amount of data generated. As more and more devices become connected to the internet, the volume of data will continue to increase exponentially.
- Velocity: The speed at which data is generated. Data is generated continuously, and it needs to be processed quickly to gain insights.
- Variety: The different types of data generated. Big Data can be structured, semi-structured, or unstructured. Structured data refers to data that is well-organized and can be easily analyzed, such as spreadsheets. Semi-structured data refers to data that has some structure, such as social media posts, while unstructured data refers to data that has no structure, such as images and videos.
Current research and applications of Big Data in agriculture
There is a growing body of research on the use of Big Data in agriculture. One recent study found that the use of Big Data in agriculture can lead to significant improvements in crop yields and reduce waste. The study found that precision agriculture technologies, such as GPS mapping and sensor networks can increase crop yields by up to 30%.
In addition to precision agriculture, Big Data is also being used to improve supply chain management in agriculture. By tracking the movement of crops from the field to the supermarket, farmers can identify inefficiencies in the supply chain and reduce waste. For example, Walmart is using blockchain technology to track the movement of produce from the farm to the store, which has led to significant reductions in waste and improved food safety.
Here are some companies that use Big Data:
- SatAgro: a Spanish company that provides satellite-based crop monitoring and analysis services for farmers. They use satellite imagery and machine learning algorithms to identify crop stress, nutrient deficiencies, and other issues that can affect crop yields. This allows farmers to take targeted actions to address these issues and improve their yields.
Syngenta: a global agribusiness company, is using big data to develop new crop varieties and improve crop yields. They are using advanced analytics and machine learning to analyze large amounts of data from field trials and genetic research, which allows them to identify the most promising crop varieties and optimize their performance.
Carbon Robotics: they introduce an autonomous weeder that combines computers using deep learning to identify and “zap” weeds with carbon dioxide lasers, mounted on a four-wheel platform powered by diesel and hydraulics. The weeder can kill over 100,000 weeds per hour with its eight laser modules. This company uses deep learning techniques with the development of sensors and camera resolution for fast development.
Soiltech: a Spanish company that provides soil analysis services using big data and machine learning algorithms. Their system includes sensors that measure soil properties such as pH, nutrient levels, and moisture content, which are then analyzed to provide recommendations for fertilization and other soil management practices. The system can also predict crop yields based on soil conditions and weather data, allowing farmers to optimize their operations for maximum efficiency.
In conclusion, Big Data is revolutionizing agriculture by providing farmers with the tools they need to optimize crop yields and reduce waste. With the use of AI and other technologies, farmers can analyze vast amounts of data to make informed decisions about when to plant, how much fertilizer to use, and when to harvest.
As the amount of data generated continues to grow, it is clear that Big Data will play an increasingly important role in agriculture and professionals from this sector need to know how to use it, that’s why this content is part of our Agribusiness MBA, MIAM.
