Applications of Mobile Machine Learning for Detecting Bio-energy Crops Flowers

Wenjun Zeng, Bakhtiar Amen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


Automated flower detection and control is important to crop production and precision agriculture. Some computer vision methods have been proposed for flower detection, but their performances are not satisfactory on platforms with limited computing ability such as mobile and embedded devices, and thus not suitable for field applications. Herein we demonstrate two de novo approaches that can precisely detect the flowers of two bioenergy crops (potatoes and sweet potatoes) and can distinguish them from similar flowers of relative species (eggplants and Ipomoea triloba) on mobile devices. In this work, a custom dataset containing 495 manually labelled images is constructed for training and testing, and the latest state-of-the-art object detection model, YOLOv4, as well as its lightweight version,YOLOv4-tiny, are selected as the flower detection models. Some other milestone object detection models including YOLOv3,YOLOv3-tiny, SSD and Faster-RCNN are chosen as benchmarks for performance comparison. The comparative experiment results indicate that the retrained YOLOv4 model achieves a considerable high mean average precision (mAP = 91%) but a slower inference speed (FPS) on a mobile device, while the retrainedYOLOv4-tiny has a lower mAP of 87% but reach a higher FPS of9 on a mobile device. Two mobile applications are then developed by directly deploying YOLOv4-tiny model on a mobile app and by deploying YOLOv4 on a web API, respectively. The testing experiments indicate that both applications can not only achieve real-time and accurate detection, but also reduce computation burdens on mobile devices.
Original languageEnglish
Title of host publicationProceedings of 20th IEEE International Conference On Machine Learning And Applications
Subtitle of host publicationICMLA 2021
EditorsM. Arif Wani, Ishwar Sethi, Weisong Shi, Guangzhi Qu, Daniela Stan Raicu, Ruoming Jin
Number of pages6
ISBN (Electronic)9781665443371
ISBN (Print)9781665443388
Publication statusPublished - 13 Dec 2021
Externally publishedYes
Event20th IEEE International Conference On Machine Learning And Applications - Virtual online, Virtual, Online, United States
Duration: 13 Dec 202116 Dec 2021
Conference number: 20


Conference20th IEEE International Conference On Machine Learning And Applications
Abbreviated titleICMLA2021
Country/TerritoryUnited States
CityVirtual, Online
Internet address


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