• Dr Ayesha Banu
• Kaitha Rajitha
• Pastam Srinivas
• Komakula swathi
• Bathini Shashikiran

Precision agriculture is focused on the detailed segmentation of the soil types in order to ensure better productivity and increased efficiency. Current manual practices in the field are not only tedious but also require proper expertise in the matter, making them both time and cost-consuming. Furthermore, the possibility of error during the process is not ruled out, thus complicating the decision-making process. This paper proposed an automated solution on the basis of advanced algorithms and artificial intelligence to address the issues introduced by conventional methods and provide a reliable, accurate, and quick alternative that is built upon the principles of convolutional networks and traditional machine learning approaches. The suggested methodology utilizes the process of image processing to convert soil images into numerical data necessary for analysis and subsequent segmentation. The collected data is utilized to train machine learning and deep learning models, including the Extra Trees Classifier and Convolutional Neural Network (CNN). To facilitate easy and intuitive user interaction, the system is implemented through a graphical user interface (GUI) developed using Tkinter, allowing individuals without technical expertise to operate the application efficiently. The system also incorporates visualization features, such as confusion matrices and training history graphs, which enable effective assessment and monitoring of model performance and accuracy. By automating the process of soil classification, the proposed system seeks to enhance conventional soil assessment practices by minimizing dependence on limited expert resources while providing rapid and precise results. It enables reliable identification of soil types within a shorter time, supporting better resource utilization and facilitating informed decision-making in agricultural activities. Overall, the proposed AI-based solution has considerable potential to improve the efficiency and effectiveness of precision agriculture, thereby promoting sustainable farming practices and contributing to enhanced crop productivity.

Precision agriculture ,Soil type classification, Machine Learning, Predictive analytics, Deep CNN.

"Soil Quality Classification using Machine Learning for Precision Agriculture", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 9, page no.d16-d22, September-2026, Available :http://www.jetir.org/papers/JETIR2609303.pdf

"Soil Quality Classification using Machine Learning for Precision Agriculture", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 9, page no. ppd16-d22, September-2026, Available at : http://www.jetir.org/papers/JETIR2609303.pdf