• Srivaramangai R
• Ananya Kamble
• Shraddha Kenjale

Sugarcane cultivation is vital for the global sugar industry, but it is threatened by various pathogens that can cause significant yield losses. Predictive analysis techniques, especially machine learning, offer a promising approach to mitigating the impact of these pathogens on sugarcane production. In this study, we propose a machine learning-based predictive analysis framework for identifying and predicting the occurrence of sugarcane pathogens. The framework involves collecting and analyzing large datasets of environmental variables, disease incidence rates, and other relevant factors to develop accurate predictive models. Various machine learning algorithms are employed to train these models and predict the likelihood of pathogen outbreaks. Through the integration of advanced analytics techniques, such as feature selection and ensemble learning, our framework aims to improve the accuracy and reliability of predictions. The results demonstrate the potential of machine learning in enhancing the management of sugarcane pathogens, enabling timely interventions and increasing the overall resilience of sugarcane crops to disease outbreaks. This research contributes to the growing body of knowledge on precision agriculture and demonstrates the capability of machine learning in addressing complex agricultural challenge.

Sugarcane, Cultivation, Detection, Analysis, Machine Learning

"Predictive Analysis of Sugarcane Pathogens: A Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.k250-k254, March-2024, Available :http://www.jetir.org/papers/JETIR2403A34.pdf

"Predictive Analysis of Sugarcane Pathogens: A Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppk250-k254, March-2024, Available at : http://www.jetir.org/papers/JETIR2403A34.pdf