• Vaishnavi Vinod Wagh
• Navleenkaur Harpreetsingh Bindra
• Nikita Keshav Khedkar
• Riddhi Santosh kalyankar
• Kishor Motiram Mahale

The advent of IoT-enabled farming systems is a result of both the quick development of technology and the rising desire for effective and sustainable agricultural practices. In this study, a machine learning (ML) algorithm is used to provide a novel method for scheduling and testing soil in IoT-enabled farms. This study's goal is to maximize the use of fertilizers and irrigation by analyzing real-time soil data obtained from IoT sensors and using ML to forecast soil moisture content and nutrient levels. Farmers may make data-driven decisions for fertilization and irrigation by utilizing the power of IoT and ML, which will increase crop output, conserve resources, and have a smaller environmental impact. In order to monitor soil factors including pH, temperature, moisture, and nutrient levels, the process entails placing IoT sensors in the field. These sensors provide the data to a central system, which analyses it and uses an ML algorithm to produce precise predictions. To improve its predictive power, the ML model integrates a number of variables, including crop kind, growth stage, and meteorological conditions, which are trained using historical soil data.

pH, soil moisture, micronutrient, crop growth.

"Soil Analysis and Crop Prediction in IoT enabled farms using ML Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.g585-g590, May-2023, Available :http://www.jetir.org/papers/JETIR2305686.pdf

"Soil Analysis and Crop Prediction in IoT enabled farms using ML Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppg585-g590, May-2023, Available at : http://www.jetir.org/papers/JETIR2305686.pdf