Chaotic Jaya Optimization Algorithm With Computer Vision-Based Soil Type Classification for Smart Farming

Smart farming helps to increase yield by smartly deciding the steps that should be practised in the season. A few components of precision farming are recommending the crops for cultivation, predicting the weather conditions, examining the soil; determining the pesticides, and fertilizers that have to be used. Smart Farming utilizes advanced technologies namely data mining (DM), machine learning (ML), the Internet of Things (IoT), and data analytics for collecting the data, predicting the outcomes and training the system. One of the most significant parameters is proper soil prediction which decides the proper crop and is manually executed by the agriculturalists. Hence, the farmer's efficacy can be improved by producing automated tools for soil type classification. This study presents a Chaotic Jaya Optimization Algorithm with Computer Vision based Soil Type Classification (CJOCV-STC) for smart farming. The presented CJOCV-STC technique applies CV with metaheuristic algorithms for the automated soil classification process, which identifies the soil into distinct types. To accomplish this, the presented CJOCV-STC technique uses the SqueezeNet model for producing a set of feature vectors. To improve the performance of the SqueezeNet model, the CJO algorithm is used for the hyperparameter tuning process. Moreover, the Elman neural network (ENN) technique is applied for soil type classification and the parameters related to it can be adjusted by the chicken swarm algorithm (CSA). The soil classification performance of the CJOCV-STC method can be studied on the Kaggle dataset and the outcomes stated the better performance of the CJOCV-STC algorithm over other recent approaches with increased accuracy of 98.47%.