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Prediction of Suitable Crops Using Stacked Scaling Conjugant Neural Classifier

P. Nithya*, A. M. Kalpana

Government College of Engineering, Salem, 636011, India

* Corresponding Author: P. Nithya. Email: email

Intelligent Automation & Soft Computing 2023, 35(3), 3743-3755. https://doi.org/10.32604/iasc.2023.030394

Abstract

Agriculture plays a vital role in economic development. The major problem faced by the farmers are the selection of suitable crops based on environmental conditions such as weather, soil nutrients, etc. The farmers were following ancestral patterns, which could sometimes lead to the wrong selection of crops. In this research work, the feature selection method is adopted to improve the performance of the classification. The most relevant features from the dataset are obtained using a Probabilistic Feature Selection (PFS) approach, and classification is done using a Neural Fuzzy Classifier (NFC). Scaling Conjugate Gradient (SCG) optimization method is used to update the weights. The data set used for analysis contain various parameters such as soil characteristics, geographical location, and environmental factors such as temperature and rainfall. The proposed method recommends suitable crops for cultivation based on site-specific parameters. Experimental result shows that the proposed method provides high accuracy and efficiency as compared to existing methodologies.

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Cite This Article

APA Style
Nithya, P., Kalpana, A.M. (2023). Prediction of suitable crops using stacked scaling conjugant neural classifier. Intelligent Automation & Soft Computing, 35(3), 3743-3755. https://doi.org/10.32604/iasc.2023.030394
Vancouver Style
Nithya P, Kalpana AM. Prediction of suitable crops using stacked scaling conjugant neural classifier. Intell Automat Soft Comput . 2023;35(3):3743-3755 https://doi.org/10.32604/iasc.2023.030394
IEEE Style
P. Nithya and A.M. Kalpana, "Prediction of Suitable Crops Using Stacked Scaling Conjugant Neural Classifier," Intell. Automat. Soft Comput. , vol. 35, no. 3, pp. 3743-3755. 2023. https://doi.org/10.32604/iasc.2023.030394



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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