• R. Esther Priya
• Pothanaboina Yuva venkat
• Kadapaka Hareesh
• Madireddy Rajasekhar Reddy
• Kolipakala Sasikala

Soil-Based Crop Advisory and Disease Control system is a combination of IoT and machine learning to improve agricultural yield and sustainability. pH, soil moisture, DHT11, and rain sensors connected to a NodeMCU extract real-time environmental information, including temperature, soil conditions, and humidity. The data is processed by the K-Nearest Neighbors (KNN) algorithm to suggest the ideal crops for an environment. In addition, a plant leaf image is taken by an ESP32-CAM module and processed by a Convolutional Neural Network (CNN) for detecting diseases. According to the analysis, the system offers actionable advice on managing disease and preventive care in the form of pesticide use, irrigation schedules, and organic treatment recommendations. All this is done in a proactive manner with the objective of reducing crop loss and enhancing general agricultural efficiency. Moreover, the deployment of IoT guarantees constant monitoring as well as real-time notifications, enabling farmers to intervene at the appropriate time. The machine learning algorithms used in the system improve their accuracy with increasing data, resulting in more precise recommendations over time. By facilitating data-driven decision-making, the project will enhance crop yield, make resource usage more efficient, and encourage sustainable farming practices. Adoption of such smart agricultural solutions plays a major role in ensuring food security, decreases the reliance on chemical-based applications, and increases environmental conservation. This technology-driven farming strategy is an important milestone towards precision agriculture, which will ensure enhanced crop health management and sustainable food production for generations to come.

IoT, Machine Learning, Agricultural Productivity, Sustainability, Soil Sensors, pH Sensor, Soil Moisture Sensor, DHT11 Sensor, Rain Sensor, NodeMCU, Real-time Data, K-Nearest Neighbors (KNN), Crop Recommendation, ESP32-CAM, Convolutional Neural Network (CNN), Disease Detection, Disease Management, Preventive Measures, Pesticide Use, Irrigation Scheduling, Organic Treatments, Precision Agriculture, Food Security, Resource Optimization, Environmental Conservation, Sustainable Farming, Crop Health Management.

"Soil Based Crop Guidance and Disease Management", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.d462-d469, March-2025, Available :http://www.jetir.org/papers/JETIR2503350.pdf

"Soil Based Crop Guidance and Disease Management", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppd462-d469, March-2025, Available at : http://www.jetir.org/papers/JETIR2503350.pdf