Decision support platform for production of chili using IoT, cloud computing, and machine learning approach

Abstract

The chili crop is largely grown in several regions of the world, especially in Asian and African countries. It is a major source of income for both small- and large-scale farmers. Unfortunately, chili farmers have to contend with the challenge of pests and diseases and the need for timely decisions to have a bountiful production. To solve this problem, this paper proposes a chili-decision support platform (chili-DSP) that can help farmers detect diseases, and nutrient deficiency and make timely decisions. The proposed system integrates the internet of things, cloud computing, and data analytics technologies. The framework and architecture of the proposed chili-DSP are presented in this paper and the preliminary results using the convolutional neural network (CNN) for the classification of chili are presented. The result shows that CNN provides an accurate prediction of the learned data set and can be extended to larger data set for real-time classification of chili diseases. The chili-DSP is expected to provide a comprehensive feature and support that will help the chili farmers enhance the production of chili while minimizing losses.