Crop Prediction and Mapping Using Soil Features With Different Machine Learning Techniques

Analysts are increasingly interested in planning and organizing land activities near the shore, a trend that has emerged in recent years. This interest is driven by various factors, particularly the increasing focus on agricultural land and research on soil health. Soil strength plays a crucial role in enhancing crop yields, making it a significant area of research focus in local regions. The research discussed in this work delves into the study of water flow, examining its potential benefits and the problems it may pose. The primary emphasis is on scientifically investigating various advanced and efficient clustering systems and methods. The goal is to understand how these methods contribute to improving the accuracy of classification. To enhance classification accuracy, it is vital to make effective use of remotely sensed data features and choose the most suitable classifier. In our project, we aim to predict crops and weather conditions like temperature, humidity, pH, and rainfall based on soil attributes such as nitrogen, phosphorus, potassium, also the season and region. We have employed the Random Forest algorithm, selecting the configuration that yields the highest prediction accuracy. Ultimately, our efforts have resulted in achieving an impressive 93.7% accuracy using the Random Forest algorithm.

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Crop Prediction and Mapping Using Soil Features With Different Machine Learning Techniques

Cited by 2 publications

References 2 publications

Performance Assessment of Machine Learning Techniques for Corn Yield Prediction

Performance Assessment of Machine Learning Techniques for Corn Yield Prediction

Crop Recommendation in Precision Agriculture Using Machine Learning Techniques

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