Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification

Accurate soil prediction is a vital parameter involved to decide appropriate crop, which commonly carried out by the farmers. Designing an automated tool helps considerably improve efficacy of At same time, fuzzy logic (FL) approaches can be used for design predictive models, particularly, Fuzzy Cognitive Maps (FCMs) have concept uncertainty representation and cognitive mapping. In other words, FCM integration recurrent neural network (RNN) FL in knowledge engineering phase. this aspect, paper introduces effective maps with cat swarm optimization classification (FCMCSO-ASC) technique. The goal FCMCSO-ASC technique identify categorize seven different types soil. To accomplish this, incorporates local diagonal extrema pattern (LDEP) as feature extractor producing collection vectors. addition, FCMCSO model applied weight values are optimally adjusted use CSO algorithm. For examining enhanced outcomes technique, series simulations were on benchmark dataset experimental reported performance over recent techniques maximum accuracy 96.84%.