Supervising and increasing crop growth potential

Josip Juraj Strossmayer University in Osijek | Croatia

Lucija Blažević | Elizabeta Konjušak | Patrick Nikić

Intention is to apply machine learning algorithms and improve agricultural production processes. This in turn would increase sustainability with effects in economy, ecology and social engagement of new technologies and field experts in community. We aim to detect anomalies in growth early in order to prevent consequences of inaction during crop growth processes. If it's possible, and we strive to prove it is, to act on time this will enable agricultural companies to improve crop quality which leads to reduction of impacts: - ecological (excessive usage of herbicides, pesticides, fertilizers and watering loss), - economical (increased crop return, profit, optimizing machine/operations equipment usage) - social (increasing awareness about positive AI/ML impacts and technical expertise in the community, building interest for cutting-edge technology between students and on university) This technology could be used in our country side to increase agricultural sustainability and give boost to this part of the country.