NSGA-2 Optimized Fuzzy Inference System for Crop Plantation Correctness Index Identication

Advanced technology in agriculture help to know about suitable environmental conditions, soil health status, water and fertilizer requirements, and crop monitoring at every plant growth stage, resulting in higher yield. In the past few decades, many countries have witnessed different rain and temperature patterns due to change in environmental conditions. The plantation schedule imparts mark-able effects on the crop yield. The correct and well-planned schedule can result in getting maximum productivity with limited resources. This study presents rule-based fuzzy classification method, for predicting the sowing fuzziness based on environmental conditions. The proposed study is a three-step procedure that identifies the sowing time of Cotton, Maize, and Groundnut. First, the knowledge and rule base of the fuzzy inference system is designed. In the second step rule base of the fuzzy inference system is optimized using multi-objective evolutionary algorithm NSGA-2, which helps maximize the accuracy and minimize the number of fuzzy rules taken for classification. Finally, the fuzziness of crop sowing in different slots is determined. Set of solutions in NSGA-2 are validated through a cross-validation approach. Further, the fuzziness of the sowing slot of Cotton, Maize, and Groundnut is correlated to yield in a given year to measure the model's effectiveness.