The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1077–1084, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1077-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1077–1084, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1077-2020

  21 Aug 2020

21 Aug 2020

TOMATOD: EVALUATION OF OBJECT DETECTION ALGORITHMS ON A NEW REAL-WORLD TOMATO DATASET

V. Tsironis, S. Bourou, and C. Stentoumis V. Tsironis et al.
  • up2metric P.C., 11521, Athens, Greece

Keywords: Object Detection, Benchmark, Precision Agriculture, Dataset, Deep Learning, Classification, TomatOD

Abstract. The integration of modern technologies in farming poses a challenging task to the research community. In this work, the task of selective cropping and treating is considered, whereas learning algorithms can provide essential assistance on crop growth and disease prediction, species recognition and fruit detection. In this paper, we introduce a highly specialized object detection (OD) and classification dataset of tomato fruits that contains class information for the ripening stage of each tomato fruit apart from its corresponding bounding box. With this dataset we aim to encourage the development of task-specific production ready object detection algorithms, as well as to evaluate and provide a baseline result of common state-of-the-art generic OD algorithms. In detail, a thorough presentation of the most common OD datasets takes place, where we discuss both generic OD and some highly specialized datasets. Our dataset contains more than 250 images and 2400 annotations in total. The dataset contains class information for three ripening stages of a tomato fruit provided by expert agriculturists, while providing views consistent with the targeted real-world use case scenario. Compared to other OD datasets our proposition differs in core areas such as the quality of the annotations, the object size distribution and the public availability. Evaluating the performance in our dataset for six object detection models we draw conclusions about the strength and weaknesses of each one’s performance. Finally, we present a future roadmap of revisions and discuss some future research topics that could improve the performance of OD algorithms in our dataset.