Classification and yield prediction in smart agriculture system using IoT

The modern agriculture industry is data-centred, precise and smarter than ever. Advanced development of Internet-of-Things (IoT) based systems redesigned smart agriculture. This emergence in innovative farming systems gradually increases crop yields, reduces irrigation wastages and making it more profitable. Machine learning (ML) methods achieve the requirement of scaling the learning performance of the model. This paper introduces a hybrid ML model with IoT for yield prediction. This work involves three phases: pre-processing, feature selection (FS) and classification. Initially, the dataset is pre-processed and FS is done on the basis of Correlation based FS (CBFS) and the Variance Inflation Factor algorithm (VIF). Finally, a two-tier ML model for an IoT based smart agriculture system is proposed. In the first tier, the Adaptive k-Nearest Centroid Neighbour Classifier (aKNCN) model is proposed to estimate the soil quality and to classify the soil samples into different classes based on the input soil properties. In the second tier, the crop yield is predicted using the Extreme Learning Machine algorithm (ELM). In the optimized strategy, the weights are updated using a modified Butterfly Optimization Algorithm (mBOA) to improve the performance accuracy of ELM with minimum error values. PYTHON is the implementation tool for evaluating the proposed system. Soil dataset is utilized for performance evaluation of the proposed prediction model. Various metrics such as accuracy, RMSE, R-2, MSE, MedAE, MAE, MSLE, MAPE and Explained Variance Score (EVS) are considered for the performance evaluation.

Automated summary

The modern agriculture industry is increasingly data-centered and smarter, with the use of IoT technology revolutionizing farming practices. This paper introduces a hybrid ML model with IoT for yield prediction, involving preprocessing, feature selection, and classification phases. The proposed system utilizes machine learning methods and IoT technology to accurately predict crop yields and optimize performance metrics.