This letter presents a three-dimensional (3-D) visual detection method for the challenging task of detecting peduncles of sweet peppers (Capsicum annuum) in the field. Cutting the peduncle cleanly is one of the most difficult stages of the harvesting process, where the peduncle is the part of the crop that attaches it to the main stem of the plant. Accurate peduncle detection in 3-D space is, therefore, a vital step in reliable autonomous harvesting of sweet peppers, as this can lead to precise cutting while avoiding damage to the surrounding plant. This letter makes use of both color and geometry information acquired from an RGB-D sensor and utilizes a supervised-learning approach for the peduncle detection task. The performance of the proposed method is demonstrated and evaluated by using qualitative and quantitative results [the area-under-the-curve (AUC) of the detection precision-recall curve]. We are able to achieve an AUC of 0.71 for peduncle detection on field-grown sweet peppers. We release a set of manually annotated 3-D sweet pepper and peduncle images to assist the research community in performing further research on this topic.
The berry size of wine-grapes has often been considered to influence wine composition and quality, as it is related to the skin-to-pulp ratio of the berry and the concentration of skin-located compounds that play a key role in the wine quality. The size and weight of wine-grapes are usually measured by hand, making it a slow, tedious and inaccurate process. This paper focuses on two main objectives aimed at automating this process using image analysis: (1) to develop a fast and accurate method for detecting and removing the pedicel in images of berries, and (2) to accurately determine the size and weight of the berry. A method to detect the peduncle of fruits is presented based on a novel signature of the contour. This method has been developed specifically for grapevine berries, and was later extended and tested with an independent set of other fruits with different shapes and sizes such as peppers, pears, apples or mandarins. Using this approach, the system has been capable of correctly estimating the berry weight (R2 > 0.96) and size (R2 > 0.97) of wine-grapes and of assessing the size of other fruits like mandarins, apples, pears and red peppers (R2 > 0.93). The proven performance of the image analysis methodology developed may be easily implemented in automated inspection systems to accurately estimate the weight of a wide range of fruits including wine-grapes. In this case, the implementation of this system on sorting tables after de-stemming may provide the winemaker with very useful information about the potential quality of the wine. Abstract Three image analysis methods were studied and evaluated to solve the problem of removing long stems attached to mechanically harvested oranges: colour segmentation based on linear discriminant analysis, contour curvature analysis, and a thinning process which involves iterating until the stem becomes a skeleton. These techniques are able to determine the presence or absence of a stem with certainty, to locate the stems from random views with more than 90 accuracy and from profile images with an accuracy ranging from 92·4 to 100 depending on the method used. Finally, determination of the length and cutting point of the stem is achieved with only 3·8 of failures. Abstract Fruit and vegetables are normally presented to consumers in batches. The homogeneity and appearance of these have significant effect on consumer decision. For this reason, the presentation of agricultural produce is manipulated at various stages from the field to the final consumer and is generally oriented towards the cleaning of the product and sorting by homogeneous categories. The project ESPRIT 3, reference 9230 ‘Integrated system for handling, inspection and packing of fruit and vegetable (SHIVA)’ developed a robotic system for the automatic, non-destructive inspection and handling of fruit. The aim of this paper is to report on the machine vision techniques developed at the Instituto Valenciano de Investigaciones Agrarias for the on-line estimation of the quality of oranges, peaches and apples, and to evaluate the efficiency of these techniques regarding the following quality attributes: size, colour, stem location and detection of external blemishes. The segmentation procedure used, based on a Bayesian discriminant analysis, allowed fruits to be precisely distinguished from the background. Thus, determination of size was properly solved. The colours of the fruits estimated by the system were well correlated with the colorimetric index values that are currently used as standards. Good results were obtained in the location of the stem and the detection of blemishes. The classification system was tested on-line with apples obtaining a good performance when classifying the fruit in batches, and a repeatability in blemish detection and size estimation of 86 and 93 respectively. The precision and repeatability of the system, was found to be similar to those of manual grading.