An integrated fuzzy rough set and real coded genetic algorithm approach for crop identification in smart agriculture

Digitalization accumulates data in a short period. Smart agriculture for crop identification for cultivation is a common problem in agriculture for agronomists. The generated data due to digitalization does not provide any useful information unless some meaningful information is retrieved from it. Therefore from the existing information system, prediction of decision for unseen associations of attribute values is of challenging. This paper presents a model that hybridizes a fuzzy rough set, real-coded genetic algorithm, and linear regression. The model works in two phases. In the initial phase, the fuzzy rough set is used to remove superfluous attributes whereas, in the second phase, a real-coded genetic algorithm is used to predict the decision values of unseen instances by making use of linear regression. The proposed model is analyzed for its viability using agricultural information system obtained from Krishi Vigyan Kendra of Thiruvannamalai district of Tamilnadu, India. Further, the accuracy of the proposed model is compared with existing techniques.

Automated summary

This paper introduces a model that combines fuzzy rough set, real-coded genetic algorithm, and linear regression for predicting decision values of unseen instances in smart agriculture. The model goes through two phases - using fuzzy rough set to eliminate unnecessary attributes in the first phase, and employing real-coded genetic algorithm with linear regression in the second phase. The viability of the proposed model is assessed using agricultural information system data from a specific district in India, and its accuracy is compared with existing techniques.