This paper tries to combat the food waste of strawberries during the harvesting steps.
An automatic pipeline must be established to combat this food waste.
One of the steps needed in this pipeline is detecting strawberries in images.
Therefore, this paper aims to find out which Convolutional Neural Network (CNN) can be best used to detect strawberries.
Faster r-cnn, Mask r-cnn and RetinaNet are compared against each other using different setting.
Mask r-cnn achieved the highest average bounding box and segmentation mAP with 51.63 and 73.20 respectively.

The purpose of this research is to reduce food waste by monitoring the ripening process of strawberries in order to optimize the harvesting time. To improve the moment of harvest, we need to know the ripeness of a strawberry. Using data from different color ranges and spaces we should be able to predict the ripeness of a strawberry on a 1-10 scale. We want to answer the question whether data from multiple color spaces can improve such a prediction model.

The prediction is performed on strawberry segments, using linear regression. The regression is performed based on the ripeness and the red and green pixel values in a segment. Each color space uses a slightly different metric.

We are able to show that the YCbCr and CIELab color space outperform RGB in such a linear regression. This is likely to come from the fact that these color spaces separate the luminance and chrominance. For the near-infrared range however, we do not have enough data to make such a conclusion as the available data only has ripeness levels 7-9.

To reduce food waste, the strawberry harvesting process should be optimized. In the modern era, computer vision can provide huge amounts of help. This paper focuses on optimizing pre-trained convolutional neural networks (CNN) to determine the maturity level of strawberries on a 1-10 scale. Here, 1 means unripe and 10 means overripe. Maturity level 8 is marketable. Experiments are done with VGG19, Resnet50, InceptionV2, Alexnet, and EfficientNetB2 as classifiers on segments using ADAM and SGD as optimizers and cross-entropy as loss function. The same CNN's are applied as a backbone for FasterRCNN to see how they would behave within an object detection architecture. The biggest challenge during this research was the low amount of training data. The research showed that using convolutional neural networks as a maturity level predictor is possible, but a well made training set with an equal spread for each maturity level is necessary to possibly achieve high accuracy.

To reduce food waste, it is important to know what strawberries to prioritise for harvesting. Size is an important quality attribute for strawberry. In order to know the size, the depth of the strawberry in the image must been known. To estimate the depth, stereovision gets utilized using binocular images. Since classic stereovision methods are quite inaccurate in predicting small areas in images, a technique by [Mustafah et al., 2013] is used. Using given segments of the strawberries, the left and right strawberry images will get matched. With the matched strawberries, the disparity can be calculated and thus the depth. Using the depth, the size can be estimated.

Strawberries have a short shelf-life time and thus need to be harvested at the right time to reduce waste. To this end, information about quality attributes is useful. Recently, many computer vision methods have been proposed. Most literature analyzes postharvest, which means that strawberries can only be analyzed after harvesting. As a result, these methods cannot be used to find a good timing to harvest. We analyze strawberries preharvest, so that we can analyze until we find a good timing to harvest. We show that predicting ripeness, sweetness, and firmness of strawberries is possible infield. Further, we analyze strawberry size to find a fitting market. Since we analyze size infield, we find two challenges: occlusions and lack of depth information. We perform inpainting to try to recover the original shape. Results are good on artificial occlusions, but varying on real occlusions as it is difficult to adapt to all kinds of occlusions. We use stereo vision and depth estimation to estimate size. Stereo vision improves size estimation slightly.