• Deeksha N P
• Deepa H Math
• B K Bindiya
• Ananya Agarwal
• Aruna U

This project is centered on providing valuable assistance to farmers by employing Machine Learning (ML) to anticipate the likelihood of drought and furnish tailored crop recommendations. To accomplish this, it harnesses the capabilities of the Weather API to collect essential parameters, including temperature, humidity, condition, wind speed, and precipitation. These vital data points are then inputted into the Random Forest Algorithm, facilitating the prediction of drought levels with precision. Consequently, the system is able to suggest crops that are best suited for varying probabilities of drought. Through meticulous evaluation of factors such as temperature, humidity, and precipitation, farmers are equipped to proactively assess drought risks and make well-informed decisions when planning their crops, thus effectively mitigating the detrimental impacts of drought on agricultural endeavours.

ML (Machine Learning),API (Application Programming Interface), Random Forest Algorithm, gTTS- Google Text-to- Speech, Weather API.

"Drought Detection and Agricultural Suggestions Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.f782-f786, May-2024, Available :http://www.jetir.org/papers/JETIR2405587.pdf

"Drought Detection and Agricultural Suggestions Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppf782-f786, May-2024, Available at : http://www.jetir.org/papers/JETIR2405587.pdf