Convolutional Neural Networks for Olive Oil Classification

The analysis of the quality of olive oil is a task that is hav-ing a lot of impact nowadays due to the large frauds that have been observed in the olive oil market. To solve this problem we have trained a Convolutional Neural Network (CNN) to classify 701 images obtained using GC-IMS methodology (gas chromatography coupled to ion mobil-ity spectrometry). The aim of this study is to show that Deep Learn-ing techniques can be a great alternative to traditional oil classification methods based on the subjectivity of the standardized sensory analy-sis according to the panel test method, and also to novel techniques provided by the chemical field, such as chemometric markers. This tech-nique is quite expensive since the markers are manually extracted by an expert. The analyzed data includes instances belonging to two different crops, the first covers the years 2014–2015 and the second 2015–2016. Both har-vests have instances classified in the three categories of existing oil, extra virgin olive oil (EVOO), virgin olive oil (VOO) and lampante olive oil (LOO). The aim of this study is to demonstrate that Deep Learning techniques in combination with chemical techniques are a good alterna-tive to the panel test method, implying even better accuracy than results obtained in previous wor

Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil

Olive oil is an important commodity in the world, and its demand has grown substantially in recent years. As of today, the determination of olive oil quality is based on both chemical analysis and organoleptic evaluation from specialized laboratories and panels of experts, thus resulting in a complex and time-consuming process. This work presents a new compact and low-cost sensor based on fluorescence spectroscopy and artificial neural networks that can perform olive oil quality assessment. The presented sensor has the advantage of being a portable, easy-to-use, and low-cost device, which works with undiluted samples, and without any pre-processing of data, thus simplifying the analysis to the maximum degree possible. Different artificial neural networks were analyzed and their performance compared. To deal with the heterogeneity in the samples, as producer or harvest year, a novel neural network architecture is presented, called here conditional convolutional neural network (Cond- CNN). The presented technology is demonstrated by analyzing olive oils of different quality levels and from different producers: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). The sensor classifies the oils in the three mentioned classes with an accuracy of 82%. These results indicate that the Cond-CNN applied to the data obtained with the low-cost luminescence sensor, can deal with a set of oils coming from multiple producers, and, therefore, showing quite heterogeneous chemical characteristics.project Innosuisse - Swiss Innovation Agency 36761.1 INNO-LSproject "SUSTAINABLE" - European Union's Horizon 2020 H2020-MSCA-RISE-2020 program 101007702project "PARENT" - European Union's Horizon 2020 H2020-MSCA-ITN-2020 program 956394Junta de Andalucia-FEDER-Fondo de Desarrollo Europeo 2018 P18-H0-470

Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil

One of the main challenges for olive oil producers is the ability to assess oil quality regularly during the production cycle. The quality of olive oil is evaluated through a series of parameters that can be determined, up to now, only through multiple chemical analysis techniques. This requires samples to be sent to approved laboratories, making the quality control an expensive, time-consuming process, that cannot be performed regularly and cannot guarantee the quality of oil up to the point it reaches the consumer. This work presents a new approach that is fast and based on low-cost instrumentation, and which can be easily performed in the field. The proposed method is based on fluorescence spectroscopy and one-dimensional convolutional neural networks and allows to predict five chemical quality indicators of olive oil (acidity, peroxide value, UV spectroscopic parameters K270 and K232, and ethyl esters) from one single fluorescence spectrum obtained with a very fast measurement from a low-cost portable fluorescence sensor. The results indicate that the proposed approach gives exceptional results for quality determination through the extraction of the relevant physicochemical parameters. This would make the continuous quality control of olive oil during and after the entire production cycle a reality.European Union?s Horizon 2020 Project H2020-MSCA-RISE-2020 101007702Junta de Andalucia-FEDER-Fondo de Desarrollo Europeo P18-H0-470

Olive-Fruit Variety Classification by Means of Image Processing and Convolutional Neural Networks

The automation of classifcation and grading of horticultural products attending to different features comprises a major challenge in food industry. Thus, focused on the olive sector, which boasts of a huge range of cultivars, it is proposed a methodology for olive-fruit variety classifcation, approaching it as an image classifcation problem. To that purpose, 2,800 fruits belonging to seven different olive varieties were photographed. After processing these initial captures by means of image processing techniques, the resulting set of images of individual fruits were used to train, and continuedly to externally validate, the implementations of six different Convolutional Neural Networks architectures. This, in order to compute the classifers with which perform the variety categorization of the fruits. Remarkable hit rates were obtained after testing the classifers on the corresponding external validation sets. Thus, it was yielded a top accuracy of 95.91% when using the Inception-ResnetV2 architecture. The results suggest that the proposed methodology, once integrated into industrial conveyor belts, promises to be an advanced solution to postharvest olive-fruit processing and classifcation

Optimizing olive disease classification through transfer learning with unmanned aerial vehicle imagery

Early detection of diseases in growing olive trees is essential for reducing costs and increasing productivity in this crucial economic activity. The quality and quantity of olive oil depend on the health of the fruit, making accurate and timely information on olive tree diseases critical to monitor growth and anticipate fruit output. The use of unmanned aerial vehicles (UAVs) and deep learning (DL) has made it possible to quickly monitor olive diseases over a large area indeed of limited sampling methods. Moreover, the limited number of research studies on olive disease detection has motivated us to enrich the literature with this work by introducing new disease classes and classification methods for this tree. In this study, we present a UAV system using convolutional neuronal network (CNN) and transfer learning (TL). We constructed an olive disease dataset of 14K images, processed and trained it with various CNN in addition to the proposed MobileNet-TL for improved classification and generalization. The simulation results confirm that this model allows for efficient diseases classification, with a precision accuracy achieving 99% in validation. In summary, TL has a positive impact on MobileNet architecture by improving its performance and reducing the training time for new tasks

Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN

Olive tree growing is an important economic activity in many countries, mostly in the Mediterranean Basin, Argentina, Chile, Australia, and California. Although recent intensification techniques organize olive groves in hedgerows, most olive groves are rainfed and the trees are scattered (as in Spain and Italy, which account for 50% of the world’s olive oil production). Accurate measurement of trees biovolume is a first step to monitor their performance in olive production and health. In this work, we use one of the most accurate deep learning instance segmentation methods (Mask R-CNN) and unmanned aerial vehicles (UAV) images for olive tree crown and shadow segmentation (OTCS) to further estimate the biovolume of individual trees. We evaluated our approach on images with different spectral bands (red, green, blue, and near infrared) and vegetation indices (normalized difference vegetation index—NDVI—and green normalized difference vegetation index—GNDVI). The performance of red-green-blue (RGB) images were assessed at two spatial resolutions 3 cm/pixel and 13 cm/pixel, while NDVI and GNDV images were only at 13 cm/pixel. All trained Mask R-CNN-based models showed high performance in the tree crown segmentation, particularly when using the fusion of all dataset in GNDVI and NDVI (F1-measure from 95% to 98%). The comparison in a subset of trees of our estimated biovolume with ground truth measurements showed an average accuracy of 82%. Our results support the use of NDVI and GNDVI spectral indices for the accurate estimation of the biovolume of scattered trees, such as olive trees, in UAV images.Russian Foundation for Basic Research (RFBR) 19-01-00215 20-07-00370European Research Council (ERC) European Commission 647038Spanish Government RYC-2015-18136Consejeria de Economia, Conocimiento y Universidad de la Junta de Andalucia P18-RT-1927DETECTOR A-RNM-256-UGR18European Research and Development Funds (ERDF) progra