Survey on Novel Approach for Crop Yield Prediction Using Machine Learning

Abstract

Agriculture relies heavily on the ability to predict crop yields. When it comes to crop yield, there are a number of factors at play. This research is focused on developing cost effective methods for predicting crop yields using available parameters like irrigation, fertilizer, and temperature. Sequential forward FS, sequential backward elimination FS, correlation-based FS, random forest variable importance, and the variance inflation factor algorithm are among the five Feature Selection (FS) algorithms discussed in this study. In general, machine learning techniques are well-suited to a specific region, so they greatly assist farmers in predicting crop yields. Crop prediction can be improved by using a new FS technique called modified recursive feature elimination (MRFE). With the help of a ranking algorithm, the MRFE technique identifies and prioritizes the most important features in a dataset.

Introduction

I. INTRODUCTION

While there have been recent advancements in agricultural crop forecasting, using a variety of technology resources, techniques, and procedures is still a difficult task to accomplish. Research in agricultural management is focused on developing cost-effective algorithms for predicting crop production based on available data, such as irrigation information, fertiliser details, and temperature. A large crop prediction data set necessitates the selection of critical traits that help identify crops that are suitable for specific land areas. The process makes use of feature selection approaches.

Historical data on crop production and weather data can be used to predict crop yield. Year, area, production, and yield are all examples of variables that could be included in a crop production dataset. Minimum temperature, maximum temperature, average temperature, precipitation, evapotranspiration, and reference crop evapotranspiration are all examples of weather variables that can be included in a dataset. These are the most important parameters in a weather dataset for predicting crop yields, but there may be others in a weather dataset.

A comprehensive picture of paddy crop output can be obtained using feature selection algorithms that identify relevant paddy field circumstances (features). Predictive models in the form of expert systems are being developed by data analysts in order to improve agricultural yield while taking into account environmental factors such as soil quality, irrigation, and land use. Crop yields are estimated using machine learning (ML) in the majority of existing systems, but little has been done to predict crop yields based on soil and environmental factors.

The features collected are fed into k-nearest neighbour (kNN), Naive Bayes (NB), decision trees (DT), support vector machines (SVM), random forests (RF), and bagging classifiers in order to predict an appropriate crop and evaluate the FS process' effectiveness. More than one method of crop prediction has been used in this study (such as the SVM and the kNN), but the most common method is the use of a single prediction model (such as the SVM). Prediction features are unique to each algorithm. For crop prediction, a suitable classifier must be found to work with the FS approach. According to soil and environmental conditions, a permutation crop data set can be used to select the most appropriate key features for feature elimination (MRFE). Because the data set does not need to be updated with each iteration, the algorithm takes less time to run. The Work of MRFE It's more effective than other FS methods. The Bagging Classifier and other Benchmark Data Sets: To ensure that the proposed MRFE approach was applicable to data sets other than crop-related data sets, the UCI Repository was searched for non-crop data sets.

II. LITERATURE SURVEY

III. THE EXISTING SYSTEM

1. In existing System, the RFE method is a wrapper-type FS method that searches for a subset of features, starting with all features in the training data set and successfully deleting features until only a small number remain.
2. The RFE technique ranks acceptable traits in order of relevance, rejecting the ones that aren't as vital.
3. This method needs an iterative process for data set updating in the feature elimination process.
4. The most challenging aspect of the RFE is updating the data set, and the greatest time is spent removing weak features.

Conclusion

In this paper, the suggested MRFE approach is applicable to both crop and non-crop data sets. When compared to previous strategies, the MRFE uses permutation and ranking to choose the most suited characteristics with improved prediction ACC in the shortest amount of time. MRFE technique in predicting the most suitable crops for cultivation using classification algorithms.

References

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Copyright

Copyright © 2023 Aditya Kamble , Kunal Rathod , Poonam Hake. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.