Detecting Coffee (Coffea Arabica L.) Sequential Flowering Events Based on Image Segmentation

Coffee sequential flowering events detection and flower density estimation is essential to predict the ripening time and yield of coffee. In this study, we detected coffee flowering events automatically based on estimated flower densities in high spatial time-series digital images, using a multi-scale region based flower segmentation method. The study area is a coffee plantation in Lujiangba in the Yunnan Province of China. There are 5 flowering events in the coffee flowering period. A digital camera obtained 24 RGB images at each shooting time of day (8:00, 9:00, 10:00, 11:00, 12:00, 13:00, 14:00, 15:00, 16:00, 17:00, 17:30) by automatic adjusting the sensor with 3 depression angles and 8 azimuth angles during the coffee flowering period from March 1st to May 31st. For segment the flowers in one image, multi-scale regions were primarily generated by equally-sized superpixel segmentation and subsequent superpixel merging process. Next, the feature vectors of each region were extracted by color moments (CM) operator and local binary patterns (LBP) operator. Afterwards, the support vector machine (SVM) classifier trained on these features was applied to recognize the flower regions. Thus, the percentage of flower pixels referred as flower proportion (FP), which can estimate the image-based flower density, was calculated in preparation for detecting flowering events of time-series images. In this stage, coefficients of Recall, Precision and intersection over union (IoU) were employed to evaluate the performance of segmentation methods on 14 test images at threes depression angles and then the best flower segmentation algorithm and the optimal angle can be determined. In the stage of flowering events detection, the FPs of different shooting time multitemporal images under the optimal depression angle were calculated and plotted. Then, a threshold of FP, K, was selected to determine whether an image is on a flowering day. To determine the best shooting time for flowering events detection, Recall, Precision and IoU were also employed to evaluate the performance of time-series images shot at 11 time of day for flowering day detection. The results show that the images shot at the depression angle of 77.5 degree is the optimal depression angle for flower segmentation and meanwhile our proposed method achieves the best performance with Recall, Precision and IoU of 84.89%, 74.83% and 65.46% respectively. In the test of flowering day detection of 11 shooting time multitemporal images, the time-series images shot at 13:00 is superior to the time-series images shot at other time of day, with Recall of 65.00%, Precision of 100% and IoU of 65.00% when the K set as 0.4%. Meanwhile, all flowering events can be detected except the fifth event which has few flowers and the FPs of time-series images can correctly indicate the flower densities of each events. In conclusion, our approach can estimate the image-based flower density and detect the coffee sequential flowering events in small fields, so the results can be used for coffee fruit maturity prediction and yield estimation.