• Dr. Ramkumar Devendiran
• Devalapalle Nikhil Reddy
• Madipalli Sri Ram Gowtham
• Gannabathula Sai Kanth Pushkar
• Gopuram Karthik

With population expansion and environmental issues, crop production is a crucial component of ensuring global food security, and it must be optimized sustainably. Machine learning methods have become an effective tool in agriculture, providing creative ideas to improve crop yield. This abstract examines the application of deep learning techniques in crop agriculture, with a focus on how these techniques have the potential to completely transform this industry. A feature of machine learning, which is a subset of machine learning, is its capacity to automatically recognize and extract patterns from huge datasets. In terms of crop production, this entails utilizing a variety of data sources, such as satellite imaging, weather records, soil information, and sensor data, to guide decision-making processes at all stages of crop cultivation. Deep learning has numerous uses, and crop monitoring is one of the main uses of deep learning in agriculture. In order to provide real-time insights into crop health, neural networks can evaluate satellite and drone footage. This allows them to spot problems like pest infestations, illnesses, and nutritional deficits. This makes it possible for farmers to take prompt remedial action, limiting crop losses and the need for chemical interventions. The ability to predict yield is another crucial component of crop production. To predict future agricultural yields, machine learning models can use past data on crop performance, including environmental factors, cultivation techniques, and yield records. The Time Series Analysis Techniques used to optimize resource management, especially by farmers, allow them to make decisions about planting, harvesting, and resource allocation that directly increase efficiency.

Agriculture, Crop Prediction, Yield Optimization, Time Series Analysis, Advanced Techniques

"Enhancing Agriculture Crop Prediction and Yield Optimization through Advanced Time Series Analysis Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.c40-c50, November-2023, Available :http://www.jetir.org/papers/JETIR2311208.pdf

"Enhancing Agriculture Crop Prediction and Yield Optimization through Advanced Time Series Analysis Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppc40-c50, November-2023, Available at : http://www.jetir.org/papers/JETIR2311208.pdf