Abstract—To optimize fruit production and improve profitability cultivators remove excess flowers and fruitlets from plants and trees in the early growing season. The proportion of the flowers to be removed is determined by the flower intensity, i.e., the total number of flowers present in a row in the greenhouse. Several automated computer vision methods have been presented to estimate flower intensity, but their overall performance is still far from satisfactory. With the aim of designing a method for flower detection which is robust to occlusions and to changes in lighting conditions and camera position, this study presents a technique in which a pre-trained Faster Region-based Convolutional Neural Network (Faster R-CNN) is fine-tuned, followed by a color-based thresholding process to detect and count tomato flowers in greenhouses. Experimental results on a dataset composed of greenhouse tomato flower images acquired under different conditions, demonstrate significantly high performance, with precision and recall of 96.02% and 93.09%, respectively. The flower count from the proposed technique is comparable with the number counted manually with an error of – 4 to 3 flowers per image.
Index Terms—agricultural engineering, computer vision, deep learning, faster R-CNN, flower detection and counting
Cite: Umme Fawzia Rahim and Hiroshi Mineno, "Tomato Flower Detection and Counting in Greenhouses Using Faster Region-Based Convolutional Neural Network," Journal of Image and Graphics, Vol. 8, No. 4, pp. 107-113, September 2020. doi: 10.18178/joig.8.4.107-113
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