Internet of Things in Precision Agriculture
Big Data in Precision Agriculture
Precision agriculture emphasizes collecting and utilizing data to make decisions for agricultural value creation. There are many different sources of big data, including ground sensors, historical data collected by governmental and non-governmental agencies, web services, and online repositories. Over the past decade, agricultural production has grown dramatically due to the increasing use of data from sensors and IoT devices. Precision agriculture datasets typically comprise diverse data related to crops, soil, and nutrients, atmospheric data, technological data such as GIS data, GPS data, and data from farm equipment such as variable rate application systems.
Big Data Analytics
Big data analytics is a methodical strategy that applies cutting-edge analytic tools to big data sets. This entails integrating two technical components, namely extensive data sets and an array of analytics tools such as data mining, statistics, AI, predictive analytics, and natural language processing (NLP), among other tools. The analysis of such data is accomplished by utilizing big data mining techniques. As data expands and proliferates, new big data tools are emerging to help companies collect, process, and analyze data at the speed needed to gain the most value.
Machine Learning
Machine learning (ML) is a technique that helps understand patterns in data. Whether descriptive, predictive, or even prescriptive, it is important to choose the correct data when constructing ML models, and doing so requires a lot of thought. In supervised learning, labeled data is used to train a model. Numerous algorithms can be used for classification, such as multiple logistic regression, support vector machines (SVM), decision trees, random forests, naive Bayes, and artificial neural networks (ANN).
Challenges of Implementing Big Data in Agriculture
Addressing the challenges, the agricultural industry can unlock the full potential of big data in agriculture and drive sustainable and profitable growth. One of the primary hurdles in leveraging big data in agriculture is the quality of the data itself. Data accuracy, completeness, and consistency are crucial for deriving meaningful insights. Errors, missing values, or inconsistencies can significantly impact the reliability of analysis results. Agriculture generates vast amounts of data from various sources, including sensors, satellite imagery, and historical records.
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