Implementation of a decision support system for prediction of the total soluble solids of industrial tomato using machine learning models

Tomato is the second most important vegetable in the world, both in terms of production and consumption. Especially for the cultivation of industrial tomato, harvest is conducted when the total soluble solids, a major quality characteristic, are as high as possible. Advancements in technology have made Decision Support Systems simpler and more applicable in an everyday basis. Data Analysis, combined with Machine Learning algorithms are considered the future of sustainable agriculture, allowing farmers to be advised about the best possible decisions for their cultivation. Farmers need to adopt this kind of technology in order to be able to know when the quality of tomatoes is at its peak, in order to gather their product from the field. The implementation of a Decision Support System to predict the total soluble solids was conducted, based on data from previous years, including quality data (pH, Bostwick, L, a/b, Mean Weight, °Brix), the type of hybrid used, weather data and soil data from the fields. Data derived from fields in 6 different regions in the northwestern Peloponnese, Greece over 6 cultivation periods, created a dataset of 33 different inputs. Thirteen different algorithms were put into evaluation in order to find the best one in terms of speed and efficiency. In this research, we developed a Decision Support System using the K-nearest algorithm, which proved to be the best for our dataset. The predicted °Brix were following the same pattern as the actual °Brix. This means that the DSS could advise the farmer about the ideal harvesting period where the °Brix will be maximized. This DSS which is using real time weather data as an input is expected to be a valuable tool for the farmers. © 202