Individual Olive Tree Detection in RGB Images

Marin, Ivana; Gotovac, S.; Papić, Vladan

doi:10.23919/softcom55329.2022.9911397

Cited by 5 publications

(2 citation statements)

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“…The DeepForest model has a wide range of tree detection applications, such as olive trees (Marin et al, 2022), orchard trees (Jemaa et al, 2022), and boreal forests (L. Bennett and Boisvert, 2022). It has also been used for automatic tree indexing of cities (Petrov et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Urban Tree Detection from Remote Sensing Data based on DeepForest Model

Dakov,

Petrova-Antonova

2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The distribution and quantity of trees within a city impacts issues such as urban heat islands, air quality and general city planning. Having an automatic procedure for cataloguing them can be a valuable aid for future urban planning and design. In this paper, the forestry surveying neural network, DeepForest, is utilised for tree detection in the urban environment. The study area covers District Lozenets, which is the greenest part of Sofia, Bulgaria. Three distinct approaches are implemented considering the urban vegetation context - a simple tree detection, a tree cluster detection and a mixing approach between the two based on approximation with Poisson Disk Sampling. The evaluation of the developed models, in terms of F-1 score, shows that the achieved results are comparable to the ones achieved by the original application of the DeepForest model. Due to the specifics of urban data, all models tended to achieve a higher precision but a lower recall than the original DeepForest. Conditions, such as shade from the sun, buildings or other trees, make the detection more challenging. The obtained results prove the feasibility of the proposed approaches, even with a small amount of labelled data. The tree cluster and mixed approaches have the potential to resolve part of the issues coming from the urban environment context of application.

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Section: Introductionmentioning

confidence: 99%

Urban Tree Detection from Remote Sensing Data based on DeepForest Model

Dakov,

Petrova-Antonova

2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Since those olive groves had different characteristics, choice of olive groves used for model creation was important for detection results. This paper is an extension of work originally presented in conference 2022 International Conference on Software, Telecommunications and Computer Networks [38]. In this work, comparing to conference paper, a more detailed explanation of multiple models creation will be given.…”

Section: Introductionmentioning

confidence: 99%

Development and Analysis of Models for Detection of Olive Trees

Marin¹,

Gotovac²,

Papić³

2023

Adv. sci. technol. eng. syst. j.

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In this paper, an automatic method for detection of olive trees in RGB images acquired by an unmanned aerial vehicle (UAV) is developed. Presented approach is based on the implementation of RetinaNet model and DeepForest Phyton package. Due to fact that original (pretrained) model used in DeepForest package has been built on images of various types of trees but without images of olive trees, original model detection was unsatisfactory. Therefore, a new image dataset of olive trees was created using sets of images chosen from five olive groves. For neural network training, individual olive trees were manually labeled, and new models were generated. Each model has been trained on different set of images from selected olive groves. Pretrained model and new models were compared and evaluated for various test scenarios. Obtained results showed high precision and recall values of proposed approach and great improvement in performance compared to the pretrained model.

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