Soil Moisture Prediction And Irrigation Control In Agriculture 4.0 With The Cat Swarm Algorithm Optimized Deep Convolutional Neural Network

Agriculture remains one of the most significant industries across the world and the technological advancements in the traditional agricultural industry led to the development of the Agriculture 4.0. The soil moisture aids in determining the amount of water that needs to be fed into the field. Though there are several advantages, the existing methods possess disadvantages such as less accurate prediction, unsustainable data model, and so on. The Cat Swarm algorithm-optimized Deep Convolutional Neural Network (CSA-optimized DCNN) is proposed for soil moisture prediction and irrigation control to tackle the challenges of conventional research and to achieve better prediction accuracy. The DCNN optimized with the CSA adds an advantage as the algorithm optimizes the hyperparameters of the DCNN to enhance the efficacy of the model. The obtained data from the sensor nodes are stored in the server and fetched when need to be processed and fed as the input to the DCNN, which is optimized with CSA optimization to enhance the performance. The research model CSA optimized DCNN is assessed with measures such as Accuracy, Sensitivity, and Specificity and achieves 96.43%, 96.12%, and 98.8% respectively.