CROP PREDICTION BASED ON CHARACTERISTICS OF THE AGRICULTURAL ENVIRONMENT USING VARIOUS FEATURE SELECTION TECHNIQUES AND CLASSIFIERS

Registration ID: IJNRD_207268

Published ID: IJNRD2311007

DOI: Click Here to Get

Mrs Dr. J. Sarada , BovillaSravani

Crops, Zigbee, Monitoring, Soil, Temperature sensors, Security, Data models

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. The results depict that an ensemble technique offers better prediction accuracy than the existing classification technique

Published Paper   E-Certificate

Mrs Dr. J. Sarada & BovillaSravani (November-2023). CROP PREDICTION BASED ON CHARACTERISTICS OF THE AGRICULTURAL ENVIRONMENT USING VARIOUS FEATURE SELECTION TECHNIQUES AND CLASSIFIERS. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(11), a62-a69. https://ijnrd.org/papers/IJNRD2311007.pdf

Volume 8 Issue 11, November-2023

Pages : a62-a69

Paper Reg. ID: IJNRD_207268

Published Paper Id: IJNRD2311007

Downloads: 000121987

Research Area: Engineering

Country: warangal, TELANGANA, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2311007.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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