Article: Spark framework-based crop yield prediction using KR-PEclat and Mish-ANFIS-GRU technique Journal: International Journal of System of Systems Engineering (IJSSE) 2024 Vol.14 No.6 pp.633 - 653 Abstract: Recent advancements have made tremendous development in various fields including the agricultural sector. Existing research methodologies predicted the crop yield only based on the soil and weather conditions which in turn degraded the efficiency of the crop yield prediction. Hence, an efficient Mish-ANFIS-GRU and DI-LDA-based crop yield prediction methodology is proposed. Initially, the data obtained from the historical dataset is pre-processed and data partitioning is performed using the KR-PEclat algorithm. The partitioned data is then fed into the spark framework. Then, data balancing is done using synthetic minority oversampling technique (SMOTE) technique to obtain the highest matching data. Next, features are extracted from the balanced data followed by the DI-LDA-based feature reduction process. The reduced features are then fed into the Mish-ANFIS-GRU classifier. Now, when the farmer enters the condition for predicting the yield of the particular crop, feature mapping is performed to provide a better prediction of the crop yield. Inderscience Publishers - linking academia, business and industry through research

Title: Spark framework-based crop yield prediction using KR-PEclat and Mish-ANFIS-GRU technique

Authors: C.G. Anupama; C. Lakshmi

Addresses: Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, 603203, Kanchipuram, Chennai, Tamil Nadu, India ' Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, 603203, Kanchipuram, Chennai, Tamil Nadu, India

Abstract: Recent advancements have made tremendous development in various fields including the agricultural sector. Existing research methodologies predicted the crop yield only based on the soil and weather conditions which in turn degraded the efficiency of the crop yield prediction. Hence, an efficient Mish-ANFIS-GRU and DI-LDA-based crop yield prediction methodology is proposed. Initially, the data obtained from the historical dataset is pre-processed and data partitioning is performed using the KR-PEclat algorithm. The partitioned data is then fed into the spark framework. Then, data balancing is done using synthetic minority oversampling technique (SMOTE) technique to obtain the highest matching data. Next, features are extracted from the balanced data followed by the DI-LDA-based feature reduction process. The reduced features are then fed into the Mish-ANFIS-GRU classifier. Now, when the farmer enters the condition for predicting the yield of the particular crop, feature mapping is performed to provide a better prediction of the crop yield.

Keywords: imputation; GRU; gated recurrent unit; LDA; linear discriminant analysis; ANFIS; adaptive neuro fuzzy interference system; SMOTE; synthetic minority oversampling technique.

DOI: 10.1504/IJSSE.2024.143699

International Journal of System of Systems Engineering, 2024 Vol.14 No.6, pp.633 - 653

Received: 09 May 2023
Accepted: 26 May 2023
Published online: 06 Jan 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Spark framework-based crop yield prediction using KR-PEclat and Mish-ANFIS-GRU technique

Keep up-to-date