Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers

Aim:

         Predict the crop recommendation for a agriculture environment using machine learning technique and also using various feature selection techniques and classifiers.

Abstract:

        Agriculture is a growing field of research. In particular, crop prediction in agriculture is critical and is chiefly contingent upon soil and environment conditions, including rainfall, humidity, and temperature .In the past, farmers were able to decide on the crop to be cultivated, monitor its growth, and determine when it could be harvested.

         Today, however, rapid changes in environmental conditions have made it difficult for the farming community to continue to do so. Consequently, in recent years, machine learning techniques have taken over the task of prediction, and this work has used several of these to determine crop yield. To ensure that a given machine learning (ML) model works at a high level of precision, it is imperative to employ efficient feature selection methods to preprocess the raw data into an easily computable Machine Learning friendly dataset. To reduce redundancies and make the ML model more accurate, only data features that have a significant degree of relevance in determining the final output of the model must be employed. Thus, optimal feature selection arises to ensure that only the most relevant features are accepted as a part of the model. Conglomerating every single feature from raw data without checking for their role in the process of making the model will unnecessarily complicate our model. Furthermore, additional features which contribute little to the ML model will increase its time and space complexity and affect the accuracy of the model’s output.

Existing System:

           This work uses the Random Over-Sampling Examples (ROSE), Synthetic Minority Over-sampling Technique (SMOTE), and Majority Weighted Minority Over sampling Technique (MWMOTE) to help balance the given dataset. Feature selection is used to find salient features from the given dataset, resulting in better performance and classification techniques that help identify the target class.

Problem Definition:

               Wrapper feature selection techniques such as the Boruta, Recursive Feature Elimination (RFE), and Modified Recursive Feature Elimination (MRFE) are used in this work to discover the
dataset’s salient features

Proposed System:

               The RFE technique is a wrapper feature selection technique that starts with the entire dataset. The ranking method crucial to the RFE technique orders the dataset from the best to the worst, based on which salient features are selected. At each iteration, it eliminates the least important features from the dataset and updates the dataset, continuing the process until the most important ones are selected. RFE is a Wrapper-type feature selection and elimination technique that employs the greedy algorithm. The RFE algorithm recursively identifies and eliminates the least relevant features from the dataset until a sophisticated level of optimization is achieved. In the Wrapper method, the feature selection process is carried out based on a core machine learning algorithm which is fit into the dataset. SMOTETomek using oversampling on a dataset.

Advantage:

             It is not known in advance how many features a model must keep. Therefore, to determine the optimal number of features, the RFE algorithm is cross-validated. Recursive Feature Elimination Cross-Validation (RFECV) works just like RFE but, in addition to RFE, it cross-validates the features, automatically selecting the features which give the best performance its also increase the accuracy.