Design of decision model for sensitive crop irrigation system

Anita Thakur, Prakriti Aggarwal, Ashwani Kumar Dubey, Ahmed Abdelgawad, Alvaro Rocha

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Agriculture Industry is highly dependent on environmental and weather conditions. Many times, crops are spoiled because of sudden changes in weather. Therefore, we need a decision model to take care the water requirement of sensitive crops of agriculture industry. The proposed work presents a novel and proficient hybrid model for sensitive crop irrigation system (SCIS). For implementation of the model, brassica crop is taken. The duration and amount of water to be supplied is based upon the weather prediction and soil condition information. The decision model is developed using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) for brassica crops. In this model, if the input data values are available in range, then ANFIS model would be preferred and if the data sets are available for training, testing and validation then ANN model would be the best choice. The soil moisture, soil status in terms of temperature and leaf wetness are the input and flow control of sprinklers is the out for SCIS. The predicted outputs are analysed to assert the suitability of the proposed approach in the brassica crops. The proposed SCIS achieved an accuracy of 91% and 99% for ANFIS and ANN models respectively.

Original languageEnglish
Article numbere13119
JournalExpert Systems
Issue number1
StatePublished - Jan 2023


  • adaptive neuro fuzzy inference system
  • artificial neural network
  • brassica crops
  • soft computing


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