4 research outputs found
Computer Vision-Aided Intelligent Monitoring of Coffee: Towards Sustainable Coffee Production
Coffee which is prepared from the grinded roasted seeds of harvested coffee
cherries, is one of the most consumed beverage and traded commodity, globally.
To manually monitor the coffee field regularly, and inform about plant and soil
health, as well as estimate yield and harvesting time, is labor-intensive,
time-consuming and error-prone. Some recent studies have developed sensors for
estimating coffee yield at the time of harvest, however a more inclusive and
applicable technology to remotely monitor multiple parameters of the field and
estimate coffee yield and quality even at pre-harvest stage, was missing.
Following precision agriculture approach, we employed machine learning
algorithm YOLO, for image processing of coffee plant. In this study, the latest
version of the state-of-the-art algorithm YOLOv7 was trained with 324 annotated
images followed by its evaluation with 82 unannotated images as test data.
Next, as an innovative approach for annotating the training data, we trained
K-means models which led to machine-generated color classes of coffee fruit and
could thus characterize the informed objects in the image. Finally, we
attempted to develop an AI-based handy mobile application which would not only
efficiently predict harvest time, estimate coffee yield and quality, but also
inform about plant health. Resultantly, the developed model efficiently
analyzed the test data with a mean average precision of 0.89. Strikingly, our
innovative semi-supervised method with an mean average precision of 0.77 for
multi-class mode surpassed the supervised method with mean average precision of
only 0.60, leading to faster and more accurate annotation. The mobile
application we designed based on the developed code, was named CoffeApp, which
possesses multiple features of analyzing fruit from the image taken by phone
camera with in field and can thus track fruit ripening in real time
Segmentation and size estimation of tomatoes from sequences of paired images
International audienceIn this paper, we present a complete system to monitor the growth of tomatoes from images acquired in open fields. This is a challenging task because of the severe occlusion and poor contrast in the images. We approximate the tomatoes by spheres in the 3D space, hence by ellipses in the image space. The tomatoes are first identified in the images using a segmentation procedure. Then, the size of the tomatoes is measured from the obtained segmentation and camera parameters. The shape information combined with temporal information, given the limited evolution from an image to the next one, is used throughout the system to increase the robustness with respect to occlusion and poor contrast. The segmentation procedure presented in this paper is an extension of our previous work based on active contours. Here, we present a method to update the position of the tomato by comparing the SIFT descriptors computed at predetermined points in two consecutive images. This leads to a very accurate estimation of the tomato position, from which the entire segmentation procedure benefits. The average error between the automatic and manual segmentations is around 4 % (expressed as the percentage of tomato size) with a good robustness with respect to occlusion (up to 50 %). The size estimation procedure was evaluated by calculating the size of tomatoes under a controlled environment. In this case, the mean percentage error between the actual radius and the estimated size is around 2.35 % with a standard deviation of 1.83 % and is less than 5 % in most (91 %) cases. The complete system was also applied to estimate the size of tomatoes cultivated in open fields