Estimation of Volume and Mass of Axi-Symmetric Fruits Using Image Processing Technique

Learning metric volume estimation of fruits and vegetables from short monocular video sequences

We present a novel approach for extracting metric volume information of fruits and vegetables from short monocular video sequences and associated inertial data recorded with a hand-held smartphone. Estimated segmentation masks from a pre-trained object detector are fused with the predicted change in relative pose obtained from the inertial data to predict the class and volume of the objects of interest. Our approach works with simple RGB video frames and inertial data which are readily available from modern smartphones. It does not require reference objects of known size in the video frames. Using a balanced validation dataset, we achieve a classification accuracy of 95% and a mean absolute percentage error for the volume prediction of 16% on untrained objects, which is comparable to state-of-the-art results requiring more elaborated data recording setups. A very accurate estimation of the model uncertainty is achieved through ensembling and the use of Gaussian negative log-likelihood loss. The dataset used in our experiments including ground-truth volume information is available at https://sst.aau.at/cns/datasets.

Vegetable Size Measurement Based on Stereo Camera and Keypoints Detection

This work focuses on the problem of non-contact measurement for vegetables in agricultural automation. The application of computer vision in assisted agricultural production significantly improves work efficiency due to the rapid development of information technology and artificial intelligence. Based on object detection and stereo cameras, this paper proposes an intelligent method for vegetable recognition and size estimation. The method obtains colorful images and depth maps with a binocular stereo camera. Then detection networks classify four kinds of common vegetables (cucumber, eggplant, tomato and pepper) and locate six points for each object. Finally, the size of vegetables is calculated using the pixel position and depth of keypoints. Experimental results show that the proposed method can classify four kinds of common vegetables within 60 cm and accurately estimate their diameter and length. The work provides an innovative idea for solving the vegetable’s non-contact measurement problems and can promote the application of computer vision in agricultural automation.

Non-Destructive Measurement of the Internal Quality of Citrus Fruits Using a Portable NIR Device

The citrus industry has grown exponentially as a result of increasing demand on its consumption, giving it high standing among other fruit crops. Therefore, the citrus sector seeks rapid, easy, and non-destructive approaches to evaluate in real time and in situ the external and internal changes in physical and nutritional quality at any stage of fruit development or storage. In particular, vitamin C is among the most important micronutrients for consumers, but its measurement relies on laborious analytical methodologies. In this study, a portable near infrared spectroscopy (NIRS) sensor was used in combination with chemometrics to develop robust and accurate models to study the ripeness of several citrus fruits (oranges, lemons, clementines, tangerines, and Tahiti limes) and their vitamin C content. Ascorbic acid, dehydroascorbic acid, and total vitamin C were determined by HILIC-HPLC-UV, while soluble solids and total acidity were evaluated by standard analytical procedures. Partial least squares regression (PLSR) was used to build regression models which revealed suitable performance regarding the prediction of quality and ripeness parameters in all tested fruits. Models for ascorbic acid, dehydroascorbic acid, total vitamin C, soluble solids, total acidity, and juiciness showed Rcv2 = 0.77-0.87, Rcv2 = 0.29-0.79, Rcv2 = 0.77-0.86, Rcv2 = 0.75-0.97, Rcv2 = 0.24-0.92, and Rcv2 = 0.38-0.75, respectively. Prediction models of oranges and Tahiti limes showed good to excellent performance regarding all tested conditions. The resulting models confirmed that NIRS technology is a time- and cost-effective approach for predicting citrus fruit quality, which can easily be used by the various stakeholders from the citrus industry.

Real-Time Size and Mass Estimation of Slender Axi-Symmetric Fruit/Vegetable Using a Single Top View Image

Among the physical attributes of agricultural materials, mass, volume, and sizes have always been important quality parameters. Previous research focused mostly on volume estimation using stereo-based approaches, which rely on manual intervention or require a multiple-cameras set up or multiple-frames captures from different viewing angles to reconstruct the three-dimensional point-cloud information. These approaches are tedious and not suitable for practical machine vision systems. In this work, we only use a single camera mounted on the ceiling of the imaging chamber, which is directly above the fruit/vegetable to capture its top-view, two-dimensional image. We developed a method to estimate the mass/volume of agricultural products with axi-symmetrical shapes such as a carrot or a cucumber. The mass/volume is estimated as the sum of smaller standard blocks, such as chopped pyramids, an elliptical cone, or a conical cone. The computed mass/volume showed good agreement with analytical and experimental results. The weight estimation error is 95% for the case of the carrot and 96.7% for the cucumber. The method proved to be sufficiently accurate, easy to use, and rotationally invariant.

Evaluation of image processing technique and discriminant analysis methods in postharvest processing of carrot fruit

The most important process before packaging and preserving agricultural products is sorting operation. Sort of carrot by human labor is involved in many problems such as high cost and product waste. Image processing is a modern method, which has different applications in agriculture including classification and sorting. The aim of this study was to classify carrot based on shape using image processing technique. For this, 135 samples with different regular and irregular shapes were selected. After image acquisition and preprocessing, some features such as length, width, breadth, perimeter, elongation, compactness, roundness, area, eccentricity, centroid, centroid nonhomogeneity, and width nonhomogeneity were extracted. After feature selection, linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were used to classify the features. The classification accuracies of the methods were 92.59 and 96.30, respectively. It can be stated that image processing is an effective way in improving the traditional carrot sorting techniques.

Classification of Kiwifruit Grades Based on Fruit Shape Using a Single Camera

This study aims to demonstrate the feasibility for classifying kiwifruit into shape grades by adding a single camera to current Chinese sorting lines equipped with weight sensors. Image processing methods are employed to calculate fruit length, maximum diameter of the equatorial section, and projected area. A stepwise multiple linear regression method is applied to select significant variables for predicting minimum diameter of the equatorial section and volume and to establish corresponding estimation models. Results show that length, maximum diameter of the equatorial section and weight are selected to predict the minimum diameter of the equatorial section, with the coefficient of determination of only 0.82 when compared to manual measurements. Weight and length are then selected to estimate the volume, which is in good agreement with the measured one with the coefficient of determination of 0.98. Fruit classification based on the estimated minimum diameter of the equatorial section achieves a low success rate of 84.6%, which is significantly improved using a linear combination of the length/maximum diameter of the equatorial section and projected area/length ratios, reaching 98.3%. Thus, it is possible for Chinese kiwifruit sorting lines to reach international standards of grading kiwifruit on fruit shape classification by adding a single camera.