Fuzzy Logic based Vegetable Price prediction in IoT

Abstract

Agriculture is an integral part of every economy with research in this domain mainly aimed at not only increasing food quality and production but also reducing expenditure while maximizing profit. Climate is a major factor that affects production of vegetables and other crops there by having direct impact on pricing. Among the main challenges faced by farmers is accurate prediction of vegetable prices. Farmers therefore need to acquire a better and advanced knowledgeable model to assist them in taking proper decisions when budgeting on how much to spend on planting so that it's proportional to the expected prices of the produce, in prediction of the climatic change effects on vegetable prices, a call for variables and parameters that show the response of vegetable prices, production, consumption and storage with weather conditions is necessary. The most common approach is using the statistical models that are trained using historical yield data with simplified weather measurements, for example average temperature in the growing season, humidly and rainfall. However, such approaches do not take into consideration the weather effects on entire vegetable lifecycle inclusive of the prices for yields necessitating the need for this study. This paper presents a study on Fuzzy based vegetable price prediction considering various changes in climatic parameters including humidity, rainfall and temperature. This system model was developed based on a 4- year vegetable price data and climatic data of the same period for Kisumu (Kenya). The results show that this model can indeed be used by farmers to predict prices and hence assist them to make informed vegetable farming and investment decisions.