Comparison of 2D and 3D convolutional neural networks in hyperspectral image analysis of fruits applied to orange bruise detection.

Recent advances in hyperspectral imaging (HSI) have demonstrated its ability to detect defects in fruit that may not be visible in RGB images. HSIs can be considered 3D images containing two spatial dimensions and one spectral dimension. Therefore, the first question that arises is how to process this type of information, either using 2D or 3D models. In this study, HSI in the 550-900 nm spectral range was used to detect bruising in oranges. Sixty samples of Thompson oranges were subjected to a mechanical bruising process, and HSIs were taken at different time intervals: before bruising, and 8 and 16 h after bruising. The samples were then classified using two convolutional neural network (CNN) models, a shallow 7-layer network (CNN-7) and a deep 18-layer network (CNN-18). In addition, two different input processing approaches are used: using 2D information from each band, and using the full 3D data from each HSI. The 3D models were the most accurate, with 94% correct classification for 3D-CNN-18, compared to 90% for 3D-CNN-7, and less than 83% for the 2D models. Our study suggests that 3D HSI may be a more effective technique for detecting fruit bruising, allowing the development of a fast, accurate, and nondestructive method for fruit sorting. PRACTICAL APPLICATION: Orange bruises can reduce the market value of food, which is why the food processing industry needs to carry out quality inspections. An effective way to perform this inspection is by using hyperspectral images that can be processed with 2D or 3D models, either with deep or shallow neural networks. The results of the comparison performed in this work can be useful for the development of more accurate and efficient bruise detection methods for fruit inspection.

Agroclimatic Evolution web application as a powerful solution for managing climate data.

Applying the AgroClimatic Evolution web application allows inquiries being made, data being collected and variables being calculated with the data acquired from different public agrometeorological stations on a single platform. Today all these stations from Murcia and Andalusia (Spain) are included, and stations elsewhere in Spain are being incorporated. This web application also offers the possibility of including each user's own stations, which increases the number and availability of data close to each farmer's plots. The data collected from stations is employed to collect daily data about weather and times, which are used to calculate the reference evapotranspiration (ETo). All the data are saved in a cloud database to later consult them and study their evolution. The data provided by all the stations are validated by applying the filters indicated in Standard UNE 500540:2004 "Automatic weather stations networks" by eliminating mistaken data that could alter correct ETo calculations. With the filtered data, and having calculated ETo, the user is provided with a comparison made with the raw data supplied by public stations. The main objective of this tool is to optimize the use of water resources available from data acquisition. Managing these data will contribute to make agriculture more sustainable and compatible with the natural environment.

Development of an Algorithm for an Automatic Determination of the Soil Field Capacity Using of a Portable Weighing Lysimeter.

The challenge today is to optimize agriculture water consumption and minimize leaching of pollutants in agro-ecosystems in order to ensure a sustainable agriculture. The use of different technologies and the adoption of different irrigation strategies can facilitate efficient fertigation management. In this respect, the determination of soil field capacity point is of utmost importance. The use of a portable weighing lysimeter allows an accurate quantification of crop water consumption and water leaching, as well as the detection of soil field capacity point. In this work, a novel algorithm is developed to obtain the soil field capacity point, in order to give autonomy and objectivity to efficient irrigation management using a portable weighing lysimeter. The development was tested in field grown horticultural crops and proved to be useful for optimizing irrigation management.

Identification of Internal Defects in Potato Using Spectroscopy and Computational Intelligence Based on Majority Voting Techniques.

Potatoes are one of the most demanded products due to their richness in nutrients. However, the lack of attention to external and, especially, internal defects greatly reduces its marketability and makes it prone to a variety of diseases. The present study aims to identify healthy-looking potatoes but with internal defects. A visible (Vis), near-infrared (NIR), and short-wavelength infrared (SWIR) spectrometer was used to capture spectral data from the samples. Using a hybrid of artificial neural networks (ANN) and the cultural algorithm (CA), the wavelengths of 861, 883, and 998 nm in Vis/NIR region, and 1539, 1858, and 1896 nm in the SWIR region were selected as optimal. Then, the samples were classified into either healthy or defective class using an ensemble method consisting of four classifiers, namely hybrid ANN and imperialist competitive algorithm (ANN-ICA), hybrid ANN and harmony search algorithm (ANN-HS), linear discriminant analysis (LDA), and k-nearest neighbors (KNN), combined with the majority voting (MV) rule. The performance of the classifier was assessed using only the selected wavelengths and using all the spectral data. The total correct classification rates using all the spectral data were 96.3% and 86.1% in SWIR and Vis/NIR ranges, respectively, and using the optimal wavelengths 94.1% and 83.4% in SWIR and Vis/NIR, respectively. The statistical tests revealed that there are no significant differences between these datasets. Interestingly, the best results were obtained using only LDA, achieving 97.7% accuracy for the selected wavelengths in the SWIR spectral range.

Analysis of the Functionality of the Feed Chain in Olive Pitting, Slicing and Stuffing Machines by IoT, Computer Vision and Neural Network Diagnosis.

Olive pitting, slicing and stuffing machines (DRR in Spanish) are characterized by the fact that their optimal functioning is based on appropriate adjustments. Traditional systems are not completely reliable because their minimum error rate is 1-2%, which can result in fruit loss, since the pitting process is not infallible, and food safety issues can arise. Such minimum errors are impossible to remove through mechanical adjustments. In order to achieve this objective, an innovative solution must be provided in order to remove errors at operating speed rates over 2500 olives/min. This work analyzes the appropriate placement of olives in the pockets of the feed chain by using the following items: (1) An IoT System to control the DRR machine and the data analysis. (2) A computer vision system with an external shot camera and a LED lighting system, which takes a picture of every pocket passing in front of the camera. (3) A chip with a neural network for classification that, once trained, classifies between four possible pocket cases: empty, normal, incorrectly de-stoned olives at any angles (also known as a "boat"), and an anomalous case (foreign elements such as leafs, small branches or stones, two olives or small parts of olives in the same pocket). The main objective of this paper is to illustrate how with the use of a system based on IoT and a physical chip (NeuroMem CM1K, General Vision Inc.) with neural networks for sorting purposes, it is possible to optimize the functionality of this type of machine by remotely analyzing the data obtained. The use of classifying hardware allows it to work at the nominal operating speed for these machines. This would be limited if other classifying techniques based on software were used.