@Article{iasc.2023.030394,
AUTHOR = {P. Nithya, A. M. Kalpana},
TITLE = {Prediction of Suitable Crops Using Stacked Scaling Conjugant Neural Classifier},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {35},
YEAR = {2023},
NUMBER = {3},
PAGES = {3743--3755},
URL = {http://www.techscience.com/iasc/v35n3/49401},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2023.030394}
}