Automated Plant Species Identification Using Leaf Shape-Based Classification Techniques: A Case Study on Iranian Maples

Mohtashamian, Mojgansadat; Karimian, Mahmood; Moola, Faisal; Kavousi, Kaveh; Masoudi‐Nejad, Ali

doi:10.1007/s40998-020-00398-2

Cited by 3 publications

(1 citation statement)

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“…For instance, plant species recognition has been a target of many research studies based on their shoot organ system, such as flowers [ 38 ], leaves [ 39 , 40 ], fruit, skin and seeds [ 41 ]. Leaves are the most common object of the previous studies because of their distinct shape and structure [ 42 , 43 ] and availability throughout the growing season. Many smartphone-based applications have been developed to facilitate plant recognition, such as ApLeaf [ 44 ], leafsnap [ 45 , 46 ], PictureThis [ 47 ] and Pl@ntNet [ 48 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating the Forest Ecosystem through a Semi-Autonomous Quadruped Robot and a Hexacopter UAV

Idrissi

Hussain

Barua

et al. 2022

Sensors

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Accurate and timely monitoring is imperative to the resilience of forests for economic growth and climate regulation. In the UK, forest management depends on citizen science to perform tedious and time-consuming data collection tasks. In this study, an unmanned aerial vehicle (UAV) equipped with a light sensor and positioning capabilities is deployed to perform aerial surveying and to observe a series of forest health indicators (FHIs) which are inaccessible from the ground. However, many FHIs such as burrows and deadwood can only be observed from under the tree canopy. Hence, we take the initiative of employing a quadruped robot with an integrated camera as well as an external sensing platform (ESP) equipped with light and infrared cameras, computing, communication and power modules to observe these FHIs from the ground. The forest-monitoring time can be extended by reducing computation and conserving energy. Therefore, we analysed different versions of the YOLO object-detection algorithm in terms of accuracy, deployment and usability by the EXP to accomplish an extensive low-latency detection. In addition, we constructed a series of new datasets to train the YOLOv5x and YOLOv5s for recognising FHIs. Our results reveal that YOLOv5s is lightweight and easy to train for FHI detection while performing close to real-time, cost-effective and autonomous forest monitoring.

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