Predict Crop Production in India Using Machine Learning Technique: A Survey

Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.

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“…[17], [40], [49], [50], [51], [63], [64], [65], [68], [70], [72], [80], [93], [94], [99], [101], [103], [104], [105], [106], [113]…”

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Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

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“…The normalization method transforms 𝜎 to 𝜎 * that falls within [A, B]. The mathematical formula for it is provided in (1).…”

Section: Normalizationmentioning

confidence: 99%

“…Choosing a crop to grow is one of the most difficult decisions that farmers must make. Crop selection is influenced by temperature, soil type, market pricing, and other variables [1]. Farmers and planters can use yield projections to make financial and administrative decisions.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Deep Learning Algorithm for Forecasting of Rice Crop Yield based on Soil Nutrition Levels

Chandraprabha

Dhanraj

2023

EAI Endorsed Scal Inf Syst

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Agriculture is critical to the development of a growing country like India. For the vast majority of the population, agriculture is their primary source of income. Crop yield estimates that are accurate and timely can give crucial information for determining agriculture policy and making investments. Crop yield forecasting and prediction will boost agricultural productivity, while crop rotation will improve soil fertility. When farmers are unaware of the soil nutrition and composition, crop yields are reduced to a minimum. To address these concerns, the proposed methodology creates an ensemble deep learning system for predicting rice crop production based on soil nutrition levels. Soil nutrients and crop production statistics are taken as the input for the proposed method. The soil nutrients dataset contains different nutrients level in the soil. Crop production statistics are the amount of crop yield in a particular area. Normalization and mean of the attribute techniques are used as pre-processing approaches to fill the missing values in the input dataset. The suggested process utilizes a stacking-based ensemble deep learning strategy termed Model Agnostic Meta-Learning (MAML) for classification. MAML receives output from three different classifiers, including Deep Neural Network (DNN), Deep Belief Network (DBN) and Support Vector Machine (SVM). Then the MAML produce the final output as how much amount of rice crop is predicted in the particular soil. The proposed method provides better accuracy of 89.5%. Thus the designed model predicted the crop yield prediction in an effective manner.

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“…Solutions in computer networks and 23 [17], [40], [49], [50], [51], [63], [64], [65], [68], security [70], [72], [80], [93], [94], [99], [101], [103], [104], [105], [106], [113], [120], [122],…”

Section: IIImentioning

confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Shukla¹

2022

IJSREM

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Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2021 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research. Keywords: Research methods in machine learning, machine learning algorithms, machine learning techniques.

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Predict Crop Production in India Using Machine Learning Technique: A Survey

Cited by 17 publications

References 29 publications

Research Methods in Machine Learning: A Content Analysis

Research Methods in Machine Learning: A Content Analysis

Ensemble Deep Learning Algorithm for Forecasting of Rice Crop Yield based on Soil Nutrition Levels

Research Methods in Machine Learning: A Content Analysis

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