Harnessing Big Data for Precision Agriculture: Improving Crop Yields and Resource Management

Precision agriculture utilizes technology to improve crop productivity while using limited resources and aims at fill the gaps of food deficiency and environmental degradation. This research will therefore recommend the big data framework that comprises IoT devices, satellite images, and big data analytics for improving decisions on agriculture. The system captures, in real-time, information regarding the state of the soil, the status of the crops and the climate which is then distilled by a machine learning engine. Overall, the observations indicate the volumes have boosted by over thirty-three percent in brand-name crops such as wheat, rice and soybeans. Efficiency was also improved in resource management through effective water use by minimizing use by 26%, fertilizer by 25 %, and pesticide through applying 33%. The prescribed use of mathematical models provided accurate results of irrigation requirements, fertilization and potential pest infestations. Consequently, the economic analysis has shown that there are a 25 % reduction in costs and 92% increase in profits per hectare in instances where precision farming was practiced. The proposed architecture helps increase productivity- related revenue and profit margins in addition to being environmentally friendly. These outcomes reflect the possibilities of big data application in agriculture, which will provide for the improvement of productivity and stability of agriculture. At the end of the study the following strategies for overcoming the challenges of implementation are presented: data protection, infrastructure expenses, and farmer education.