DATA ANALYTICS FOR SMART AGRICULTURE: INTEGRATING COMPUTER SCIENCE AND AGRICULTURAL SCIENCES

Abstract

Data analytics is becoming immensely important in the field of smart agriculture to This chapter will provide an overview of data analytics in smart agriculture, focusing on how data can be collected, analysed, and used to improve crop yields, reduce costs, and increase sustainability. The challenges associated with data collection and analysis in agriculture including issues related to data quality, privacy, and security will be discussed. Some of the most promising applications of data analytics in smart agriculture, including precision agriculture, predictive analytics and remote monitoring will also be elucidated. improve farming efficiency, provide accurate real time estimates and thereby increase yields. The application of modern technologies such as sensors, internet of things (IoT) devices, and other technologies like unmanned aerial vehicle (UAV), blockchain methodology, etc. generate insights about crop growth, soil health, patterns, and other factors affecting agricultural production. With this data, farmers can confidently determine the optimal timing for planting, fertilizing, and harvesting their crops, and identify the specific areas of their land that need focused attention. Data analytics can also be used to optimize resource allocation, such as water usage, to reduce waste and minimize environmental impact. Overall, the use of data analytics in smart agriculture has the potential to revolutionize the way we produce food, making it more sustainable, efficient, and resilient in the face of climate change and other challenges.