Scouting of whiteflies in tomato greenhouse environment using deep learning

Abstract

This study shows the possibilities of how to replace tedious human labor—scouting of yellow sticky traps (YST) for whiteflies—using artificial cognitive vision, specifically the deep convolutional network (CNN), as a part of the more complex system—BERABOT. The used CNN is the Faster R-CNN trained by deep transfer learning to substitute human scouting when the low whiteflies infection phase was specifically targeted. The training was conducted on pictures taken inside the heated and lighted tomato production greenhouse of “Bezdínek Farm” in Dolni Lutyne, Czechia. Used pictures were collected in a way planned for future fully automated robotic applications in the BERABOT system. The achieved results were compared with the scouting results of a professional phytopathologist. The trained employee’s scouting results against the professional phytopathologist accomplished root-mean-square error (RMSE) equal to 4.23, while the developed CNN model was evaluated to be 5.83. The results presented here open up new frontiers for further CNN model tuning leading to the potential in substituting an employee(s) in the future and make tomato production less expensive and less human labor dependent. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.