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.

[1]  Maria-Cristina Marinescu,et al.  Original papers: A novel methodology for the monitoring of the agricultural production process based on wireless sensor networks , 2011 .

[2]  Q. Yang,et al.  Classification of apple surface features using machine vision and neural networks , 1993 .

[3]  Oscar Urbano,et al.  Cost-Effective Implementation of a Temperature Traceability System Based on Smart RFID Tags and IoT Services , 2020, Sensors.

[4]  Chenglin Wang,et al.  Real-time detection of surface deformation and strain in recycled aggregate concrete-filled steel tubular columns via four-ocular vision , 2019, Robotics Comput. Integr. Manuf..

[5]  Sparsh Mittal,et al.  A Survey on optimized implementation of deep learning models on the NVIDIA Jetson platform , 2019, J. Syst. Archit..

[6]  Pedro Sánchez,et al.  Wireless Sensor Networks for precision horticulture in Southern Spain , 2009 .

[7]  Hassan Ghassemian,et al.  Applied machine vision and artificial neural network for modeling and controlling of the grape drying process , 2013 .

[8]  Xiangjun Zou,et al.  Color-, depth-, and shape-based 3D fruit detection , 2019, Precision Agriculture.

[9]  Antonio Ruiz-Canales,et al.  Deep learning in olive pitting machines by computer vision , 2020, Comput. Electron. Agric..

[10]  Gaston Lefranc,et al.  Olive Fruits Recognition Using Neural Networks , 2013, ITQM.

[11]  Xiangjun Zou,et al.  Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field , 2019, Sensors.

[12]  Xiangjun Zou,et al.  In-field citrus detection and localisation based on RGB-D image analysis , 2019, Biosystems Engineering.

[13]  Limor Fried.  Making machine learning arduino compatible: A gaming handheld that runs neural networks - [Resources_Hands On] , 2019, IEEE Spectrum.

[14]  Giacomo Indiveri,et al.  A Scalable Multicore Architecture With Heterogeneous Memory Structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs) , 2017, IEEE Transactions on Biomedical Circuits and Systems.

[15]  Hong Wang,et al.  Loihi: A Neuromorphic Manycore Processor with On-Chip Learning , 2018, IEEE Micro.

[16]  Manfred Glesner,et al.  An alternative approach for generation of membership functions and fuzzy rules based on radial and cubic basis function networks , 1995, Int. J. Approx. Reason..

[17]  Stefano Mancuso,et al.  Identifying Olive (Olea europaea ) Cultivars Using Artificial Neural Networks , 1999 .

[18]  Jaime Lloret,et al.  IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture , 2020, Sensors.

[19]  David Bol,et al.  A 0.086-mm$^2$ 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS , 2018, IEEE Transactions on Biomedical Circuits and Systems.

[20]  Xiangjun Zou,et al.  High-accuracy multi-camera reconstruction enhanced by adaptive point cloud correction algorithm , 2019, Optics and Lasers in Engineering.