Enhancing Fertility Detection Accuracy Through Machine Learning: A Comprehensive Analysis and Novel Approaches

This paper provides a unique method that uses machine learning approaches to improve the accuracy of fertility identification in cotton crop management. The study emphasizes how crucial precise soil fertility forecasting is to maximizing productivity, making the most use of available resources, and promoting sustainable agriculture. Though useful, traditional methods frequently have drawbacks like labor intensity, spatial unpredictability, and time consumption. The study investigates the integration of sophisticated algorithms—such as Support Vector Machines, Random Forests, and Naive Bayes—with information from IoT devices, remote sensing, and past crop performance in order to address these issues. The study shows how machine learning models may lower expenses, automate data collecting, and increase forecast accuracy through a thorough investigation. Significant gains in memory, F1 scores, and precision are shown by the experimental results, offering a solid foundation for data-driven agricultural decision-making. The results imply that integrating machine learning with fertility detection can result in more productive and scalable farming methods, which would eventually improve crop yields and resource management.