Big Data Analytics in precision agriculture

Precision farming relies upon the use of technology for the betterment of farm work, but conventional methods cannot handle complicated, real-time data. The chapter addresses Big Data Analytics in precision farming to further enhance decision-making for yield forecasting, disease identification, and resource allocation. The study utilizes multisource datasets like satellite images, Internet of Things (IoT) sensor readings, and climatic trends using machine learning algorithms like Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Random Forest (RF). Optimization techniques were also employed for irrigation and resource allocation. Experimental results showed that the RF model generated the highest accuracy in predicting yield (R² = 0.89) and disease risk detection (F1 score = 93.1%). The findings signify the superior performance of ensemble learning algorithms in dealing with agricultural variability. The proposed system supports data-driven decision-making, reducing resource wastage, and improving crop yield. The analysis in this chapter confirms that the application of predictive and prescriptive analytics in agriculture can significantly encourage sustainable and intelligent farm management.