# Crop Prediction Model Using Machine Learning Algorithms
## 5 Methodology  
In our research endeavor, we deployed a comprehensive array of 15 diverse machine learning algorithms to construct models based on agricultural data, including 2200 records encompassing 22 distinct crop labels. These models provide farmers with recommendations on the most suitable crops to cultivate. Our methodology for crop analysis in Figure 3 adheres to the standard stages of data analysis [53; 54; 55; 56]. A significant improvement is the inclusion of multiple classifiers, which are fine-tuned and evaluated to identify the most suitable ones for the input data. Moreover, our methodology incorporates feature reduction and augmentation techniques [57], essential for highlighting pertinent features that enhance crop detection and assist farmers in selecting the most appropriate features for accurate predictions. In our study, we proposed applying various machine learning algorithms with different features for crop analysis. These algorithms were carefully chosen based on their unique capabilities and characteristics and included the Naive Bayes Classifier, Random Forest, and Multilayer Neural Network. We thoroughly analyzed the results, emphasizing the essential features contributing to high accuracy. To ensure high-quality data, we emphasize the importance of data collection and preprocessing, including tasks such as cleaning and transformation. Figure 3 provides an overview of our crop analysis and prediction of our methodology, incorporating IoT and machine learning algorithms. Figure 3: Methodology for crop prediction using IoT and ML. We have the following steps in our models for crop prediction. * Data Collection: The collection of data from IoT devices on farms is vital for conducting machine learning analysis. By gathering crucial information on crop usage, crop type, water requirements, and harvest methods for agriculture, we can significantly increase productivity on smart farms. * Data Modeling: The accuracy of data analysis was tested through experiments that involved altering labels. To group crops, we categorized them into four broad groups based on various factors, rather than predicting individual crop types. We analyzed a dataset of crop types using machine learning, utilizing seven different features to classify them. Additionally, we determined the minimum number of features necessary for precise learning and prediction. * Model Evaluation and Interpretation: We want to achieve precise crop detection by selecting appropriate features for our machine learning algorithms. To ensure optimal results, we considered parameter properties on the relevant properties. This allowed us to obtain accurate and reliable data for our agricultural operations. During our experiments, we analyzed the effects of modifying the labels on the accuracy of our data analysis algorithm. This enabled us to understand the impact of minor label changes better and helped us optimize our approach toward achieving greater accuracy in our results. To achieve success in crop classification, it is vital to utilize broader labels. It is crucial to thoroughly investigate and ascertain the most efficient classification techniques within this domain. Several classification algorithms, such as Naive Bayes Classifiers, Random Forest, and Multilayer Neural Network, are used to build a model to predict the correct labels based on provided data. The constructed model was initially achieved by training using training data; then, the results were evaluated using test data to ensure that predictions were accurate and desired values. The following algorithms were used in our experiment:  
* Naive Bayes Classifier is a supervised learning algorithm that uses [PERSON] theorem to classify objects. It is used in machine learning and data mining applications for text analysis, medical diagnosis, spam filtering, and other similar tasks. Naive Bayes assumes that features in a class are considered independent of each other.