• Jasmeet kaur

With the global population on the rise, agriculture faces mounting pressure to meet the food needs of the growing number of people. Precision agriculture, also called smart farming, has emerged as an innovative solution to tackle present challenges in the agricultural sector. Machine learning models are crucial in using data-driven insights to make informed decisions in different areas of agricultural operations. This paper provides a thorough summary of machine learning algorithms in the field of agriculture. The primary areas of focus involve predicting soil properties such as organic carbon and moisture levels, forecasting crop production, pinpointing crop diseases and weeds, and identifying various species. Advanced irrigation techniques like drip irrigation, along with smart harvesting methods, greatly reduce the necessity for manual labor. Ongoing advancements in ML algorithms and agricultural technologies have the potential to enhance global food security, environmental sustainability, and economic prosperity.

Machine learning, IoT, Agriculture, Smart farming, prediction.

"Machine learning applications in precision farming", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.l9-l12, April-2024, Available :http://www.jetir.org/papers/JETIR2404B02.pdf

"Machine learning applications in precision farming", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppl9-l12, April-2024, Available at : http://www.jetir.org/papers/JETIR2404B02.pdf