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The quality of fresh apple fruits is a major concern for consumers and manufacturers. Classification of these fruits according to their ripening stage is one of the most decisive factors in determining their quality. In this regard, the aim of this work is to develop a new method for non-destructive classification of the ripening state of Fuji apples using hyperspectral information in the visible and near-infrared (Vis/NIR) regions. Spectra of 172 apple samples in the range from 450 to 1000 nm were studied, which were selected from four different ripening stages. A convolutional neural network (CNN) model was proposed to perform the classification of the samples. The proposed method was compared with three alternative methods based on artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (KNN). The results revealed that the CNN method outperformed the alternative methods, achieving a correct classification rate (CCR) of 96.5%, compared with an average of 89.5%, 95.93%, and 91.68% for ANN, SVM, and KNN, respectively. These results will help in the development of a new device for fast and accurate estimation of the quality of apples. [ABSTRACT FROM AUTHOR]

Non-destructive estimation of the constituent properties of fruits and vegetables has led to a dramatic change in the agriculture and food industry, allowing accurate and efficient sorting of the products based on their internal properties. Therefore, the present study utilized visible (VIS) and near-infrared (NIR) spectroscopy data in the range from 200 to 1100 nm for the estimation of several properties of Red Delicious apples, namely Brix minus acid (BrimA), firmness, acidity and starch content, using a hybrid of Artificial Neural Networks and Artificial Bee Colony (ANN–ABC) algorithm. Furthermore, the hybrid Artificial Neural Network–Particle Swarm Optimization (ANN–PSO) algorithm was utilized to select the most effective properties to estimate these characteristics. The results indicated that there are different peaks within this spectral range, and the spectral range for each peak gives different results. To ensure the stability of the proposed method, 1000 replications were performed for each estimate. The highest coefficients of determination, R2, for estimating the studied properties among the 1000 replicates were 0.898 for BrimA, 0.8 for firmness, 0.825 for acidity and 0.973 for starch content. The selection of the most effective wavelengths for estimating the properties produced five effective wavelengths for BrimA, nine for firmness, seven for acidity and five for starch content. In this case, the best R2 of the hybrid ANN–ABC among the 1000 iterations were 0.828, 0.738, 0.9 and 0.923, respectively. [ABSTRACT FROM AUTHOR]

The estimation of the ripening state in orchards helps improve post-harvest processes. Picking fruits based on their stage of maturity can reduce the cost of storage and increase market outcomes. Moreover, aerial images and the estimated ripeness can be used as indicators for detecting water stress and determining the water applied during irrigation. Additionally, they can also be related to the crop coefficient (Kc) of seasonal water needs. The purpose of this research is to develop a new computer vision algorithm to detect the existing fruits in aerial images of an apple cultivar (of Red Delicious variety) and estimate their ripeness stage among four possible classes: unripe, half-ripe, ripe, and overripe. The proposed method is based on a combination of the most effective color features and a classifier based on artificial neural networks optimized with genetic algorithms. The obtained results indicate an average classification accuracy of 97.88%, over a dataset of 8390 images and 27,687 apples, and values of the area under the ROC (receiver operating characteristic) curve near or above 0.99 for all classes. We believe this is a remarkable performance that allows a proper non-intrusive estimation of ripening that will help to improve harvesting strategies. [ABSTRACT FROM AUTHOR]

Due to the changes in the lighting intensity and conditions throughout the day, machine vision systems used in precision agriculture for irrigation management should be prepared for all possible conditions. For this purpose, a complete segmentation algorithm has been developed for a case study on apple fruit segmentation in outdoor conditions using aerial images. This algorithm has been trained and tested using videos with 16 different light intensities from apple orchards during the day. The proposed segmentation algorithm consists of five main steps: (1) transforming frames in RGB to CIE L*u*v* color space and applying thresholds on image pixels; (2) computing texture features of local standard deviation; (3) using intensity transformation to remove background pixels; (4) color segmentation applying different thresholds in RGB space; and (5) applying morphological operators to refine the results. During the training process of this algorithm, it was observed that frames in different light conditions had more than 58% color sharing. Results showed that the accuracy of the proposed segmentation algorithm is higher than 99.12%, outperforming other methods in the state of the art that were compared. The processed images are aerial photographs like those obtained from a camera installed in unmanned aerial vehicles (UAVs). This accurate result will enable more efficient support in the decision making for irrigation and harvesting strategies. [ABSTRACT FROM AUTHOR]

• A new non-destructive ripeness estimation method for Fuji apples is presented. • Ensemble classifiers combined with the majority rule are applied on spectral data. • Narrow spectral bands prove to be superior to full visible/near-infrared and color. • The obtained classification accuracy is 99.37% for the 465–485 nm band. Non-destructive determination of the different stages of fruit ripening has important advantages over traditional methods, such as selective robotic harvesting or adapting fertilization operations depending on the ripening stage. In this regard, the purpose of the present study was to investigate the non-destructive estimation of the ripening stages of Fuji apples combining different classifiers with the majority voting (MV) method. This process is based on five constituent classifiers, including hybrids of artificial neural network (ANN) classifiers adjusted with the genetic algorithm, the particle swarm optimization algorithm and the firefly algorithm, and classifiers based on support vector machines, and the k-nearest neighbor algorithm. The input of the MV classifiers consists of four alternatives: (1) color data extracted from the second channel of L*a*b* color space, and the hue angle in L*a*b*; and multispectral data including wavelengths ranging: (2) from 465 to 485 nm; (3) from 675 to 700 nm; and (4) from 870 to 890 nm. The first two ranges are in the visible spectrum, while the second is within the near-infrared. To evaluate the reliability of the MV method, the classification procedure was repeated 1000 times with different seeds. In order to assess the obtained performance, the proposed method has been compared with an alternative technique based on an ANN classifier, in this case using all the spectral data in the range from 450 to 1000 nm, and with the hyperparameters adjusted by a grid search. The results indicate that the correct classification rate of the MV method using color data, and using spectral data from 465 to 485 nm, 675 to 700 nm, and 870 to 890 nm were 95.12%, 99.37%, 97.56% and 97.80% respectively, while the correct classification rate of the ANN method including all the spectral data from 450 to 1000 nm reached an average classification of 92.12%. Thus, the optimal selection is the MV method using spectral information from 465 to 485 nm, which is able to achieve a very accurate result, feasible to be used in practical applications. [ABSTRACT FROM AUTHOR]