Thrips incidence prediction in organic banana crop with Machine learning

Abstract

The organic banana is one of the most popular products worldwide and its popularity is mainly due to its excellent nutritional properties and tasty flavor. Peru is considered one of the major producers and exporters of this product, being the city of Piura the main region with most of the national agro-producers. It is also considered a key factor in the development of the economy of this region as it creates job opportunities because of the productive chain required in the process (harvest, post-harvest, and export). The main problem faced by producers is the existence of pests such as Red spot thrips, Black Sigatoka, and others, which affect the production and the quality of the final product. Therefore, this article aims to propose an alternative solution, using the 4.0 Industry technology as well as the installation of an IoT sensor network in banana plantations in order to develop a model which estimates the classification of the pest incidence level based on Machine learning techniques, making use of the atmospheric variables measured with the IoT sensor network as input data. In the research, we have used The Support Vector Machine techniques, which have successfully achieved models with a high level of accuracy. The implementation of this system aims to help producers improve the management of pest control by scheduling spraying dates more effectively, optimizing not only the quality of the product but also reducing costs.

Keywords

• Multi-class classification
• Machine learning
• Organic banana pest
• Support vector machine
• Red rust thrips
• Twin Support Vector Machine