Multi-sensor-based Environmental Forecasting System for Smart Durian Farms in Tropical Regions

Abstract

Durians are among the most important fruit products in tropical countries. The environments of durians therefore must support a high yield to meet demand. Sunlight, temperature, and rainfall are all key variables, and any adverse factors will have a negative impact on production. We propose an environmental prediction system for a durian farm on the basis of the concept of the IoT. The system uses multiple machine learning algorithms to analyze collected environmental data and predict the next state of the environmental variables. From numerous experiments, our results show that the support vector machine (SVM) gives the best forecasts for temperature, whereas the convolutional neural network (CNN) performs best for predicting soil humidity. The results of this paper can provide farmers with real-time understanding of their farms and early warning of potential risks. The farm yield rates can hence be increased.

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“…In addition, after data collection by IoT, Quiroz et al [39] applied a convolutional neural network (CNN) to classify the crop. Rezk et al [40], Rodríguez et al [41], and Kuo et al [42] predicted or analyzed the crop yield by using wrapper partial decision tree algorithm (WPART), extreme gradient boosting (XGBoost), and support vector machine (SVM) as well as CNN models, respectively. Hsu et al [37] not only built a model for predicting yields but also developed a subsystem for detecting unauthorized entry to crop fields.…”