A Smart and Mechanized Agricultural Application: From Cultivation to Harvest

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Kiani F., Randazzo G., Yelmen I., Seyyedabbasi A., Nematzadeh S., Anka F. A., ...More

Applied Sciences (Switzerland), vol.12, no.12, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 12 Issue: 12
  • Publication Date: 2022
  • Doi Number: 10.3390/app12126021
  • Journal Name: Applied Sciences (Switzerland)
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Keywords: autonomous robots, remote sensing, smart agriculture, climate change, environmental protection, drone, metaheuristic, Internet of Things, OPTIMIZATION, ALGORITHM, INTERNET, THINGS
  • Istanbul University Affiliated: Yes


© 2022 by the authors. Licensee MDPI, Basel, Switzerland.Food needs are increasing day by day, and traditional agricultural methods are not respond-ing efficiently. Moreover, considering other important global challenges such as energy sufficiency and migration crises, the need for sustainable agriculture has become essential. For this, an integrated smart and mechanism-application-based model is proposed in this study. This model consists of three stages. In the first phase (cultivation), the proposed model tried to plant crops in the most optimized way by using an automized algorithmic approach (Sand Cat Swarm Optimization algorithm). In the second stage (control and monitoring), the growing processes of the planted crops was tracked and monitored using Internet of Things (IoT) devices. In the third phase (harvesting), a new method (Reverse Ant Colony Optimization), inspired by the ACO algorithm, was proposed for harvesting by autonomous robots. In the proposed model, the most optimal path was analyzed. This model includes maximum profit, maximum quality, efficient use of resources such as human labor and water, the accurate location for planting each crop, the optimal path for autonomous robots, finding the best time to harvest, and consuming the least power. According to the results, the proposed model performs well compared to many well-known methods in the literature.