SSUGT created a «smart» system for determining the ripeness of tomatoes in greenhouses
The Siberian State University of Geosystems and Technologies has presented an innovative development that can significantly improve the efficiency of industrial greenhouses. Student Artem Udalov has created a software module that uses machine vision technologies to automatically assess the ripeness of tomatoes.
The relevance of the development is due to the need to minimize the dependence on the human factor in assessing the ripeness of fruits. Traditional methods of visual control require significant time and do not always provide high accuracy. The implementation of the software module will automate the process, reduce crop losses and increase the economic efficiency of greenhouses. The module analyzes images of fruits, determines the stage of their ripeness and even predicts the optimal time for harvesting, which is especially important for large agro-industrial enterprises.
The development is based on modern machine learning and computer vision technologies. The module is capable of not only assessing the ripeness of tomatoes, but also detecting signs of plant diseases, which makes it a universal tool for agronomists. In the future, it is planned to expand the functionality of the system by adding support for other crops, such as cucumbers, bell peppers and eggplants. This will make the solution even more popular in agriculture.
The module architecture includes several key components: an image processing system, deep learning algorithms for classifying maturity stages, and a user interface for easy interaction with data. The demonstration of the module showed its high accuracy and speed, which confirms the practical applicability of the development.
The development of a SSUGT student demonstrates how modern technologies can be successfully integrated into traditional industries, such as agriculture, opening up new prospects for increasing their efficiency. The implementation of such solutions can become an important step towards the digitalization of the Russian agro-industrial complex.
