Blog 26 (Big Data in Agricultural Science)

 

Blog 26: Big Data in Agricultural Science

Agricultural science has improved through big data by helping farmers increase crop production and reduce waste. Sensors placed in farms collect information about soil quality, weather conditions, and crop health.

Farmers use drones and satellite imagery to monitor crops and detect diseases early. Companies such as John Deere develop smart farming technologies that use data analytics.

Big data also helps scientists develop better farming techniques to feed growing populations.

Visualization:

Smart Farming Data Tools

ToolPurpose
DronesCrop monitoring
SensorsSoil analysis
SatellitesWeather tracking
AI SystemsPredict crop yield

Big data continues to transform scientific research by helping scientists solve complex global challenges.

Comments

Post a Comment

Popular posts from this blog

Blog 1 Big Data: Transforming Information into Knowledge

 Benefits of Big Data and How It Defines Me Big Data helps businesses and organizations make better decisions by analyzing large amounts of information to find patterns and trends. It also improves customer experience by allowing companies to offer personalized products and services. For example, companies like Amazon and Netflix use Big Data to recommend products and movies based on users' preferences. Big Data also supports innovation and efficiency by helping organizations improve services, develop new products, and reduce costs. How Big Data Defines Me Big Data affects my daily life by providing personalized recommendations when I search online, watch videos, or use social media. It also helps me access information quickly and learn new things. Overall, Big Data shapes how I use technology and interact with information every day.
Read more

Big data Blog 37 to 39

 Blog 37: The Hidden Limitations of Predictive Analytics in Modern Business Predictive analytics has become a cornerstone of data‑driven decision‑making. Companies rely on it to forecast sales, detect fraud, optimize supply chains, and personalize customer experiences. But despite its power, predictive analytics is far from perfect — and understanding its limitations is essential for using it responsibly. 1. Data Quality Issues Predictive models are only as good as the data they learn from. Incomplete, outdated, or biased datasets can lead to inaccurate predictions. When historical data contains errors, the model simply amplifies them. 2. Overfitting and Model Rigidity Some models perform extremely well on training data but fail in real‑world scenarios. This happens when the model becomes too tailored to past patterns and cannot adapt to new conditions. 3. Unpredictable Human Behavior Human decisions, emotions, and social trends can shift rapidly. Predictive analytics struggles wit...
Read more

Big Data 33

Blog 33: High-Capacity Data Storage Systems One of the most important technological requirements of big data is having enough storage capacity to handle extremely large amounts of information. Traditional storage systems are often unable to manage the massive volume of data generated every second from social media platforms, online shopping websites, healthcare systems, and smart devices. As a result, organisations require advanced storage technologies that can store structured, semi-structured, and unstructured data efficiently. Cloud storage has become one of the most popular solutions for big data storage. Companies such as Amazon Web Services , Google Cloud , and Microsoft Azure provide scalable storage services that allow businesses to expand their storage space whenever needed. This flexibility reduces costs because companies only pay for the storage they use. Distributed storage systems are also essential. Technologies such as Hadoop Distributed File System (HDFS) divide large...
Read more