Machine Learning-Based Approaches for NPK Prediction and Model Validation in Agricultural Applications

Crop prediction is a very important issue in agriculture. In case of conventional farming techniques, farmers are growing similar types of crops in the season and also applying the wrong quantity of fertilizers. So, when farmers are not attentive to soil composition and nutrition, it can lead to minimal crop production. Understanding and managing soil quality is crucial for optimizing crop yields and ensuring agricultural success. To expect a good amount of yield, it is very important to analyze the several related attributes including temperature, location, and pH value, as the pH value determines the soil’s alkalinity. Similarly, proportion ratios of nutrients nitrogen (N), phosphorous (P), and potassium (K) present in the soil of the particular region are very important parameters to be analyzed to provide the most appropriate crop recommendations. The percentage of these nutrients depends on the various parameters like crop, soil, and weather condition which is very important for providing the most appropriate farming in a particular region. The main aim of this paper is to introduce a double filter mechanism designed to enhance the model validation process, ultimately leading to accurate predictions of NPK (nitrogen, phosphorous, and potassium) values. So, here a recommendation model based on machine learning is proposed that will predict the required amount of macronutrients (NPK) ratio that will be used to recommend suitable fertilizer to be planted depending on soil and atmospheric parameters. Next, our primary focus has been on validating the classifier model which allows us to determine the correctness and effectiveness of the classifier. We employed a clustering algorithm as a validation tool for the classifier to assess and verify the performance and accuracy of the classifier’s predictions. Therefore, there exists a gap between prediction of the model and its practical application. This paper serves as a bridge that addresses the gap in the selection procedure and providing a comprehensive approach that connects the theoretical aspects of the model to its real-world application. Hence by utilizing our system, farmers can increase crop production and farmer revenue, and avoid soil pollution.