Guava Fruit Detection and Classification Using Mask Region-Based Convolutional Neural Network

Guava has various types and each type has different nutritional content, shapes, and colors. It is often difficult for some people to recognize guava correctly with so many varieties of guava on the market. In industry, the classification and segmentation of guava fruit is the first important step in measuring the guava fruit quality. The quality inspection of guava fruit is usually still done manually by observing the size, shape, and color which is prone to mistakes due to human error. Therefore, a method was proposed to detect and classify guava fruit automatically using computer vision technology. This research implements a Mask Region-Based Convolutional Neural Network (Mask R-CNN) which is an extension of Faster R-CNN by adding a branch that is used to predict the segmentation mask in each region of interest in parallel with classification and bounding box regression. The system classifies guava fruit into each category, determines the position of each fruit, and marks the region of each fruit. These outputs can be used for further analysis such as quality inspection. The performance evaluation of guava detection and classification using the Mask R-CNN method achieves an mAR score of 88%, an mAP score of 90%, and an F1-Score of 89%. It can be concluded that the proposed method performs well in detecting and classifying guava fruit

Synthetic Data for Object Classification in Industrial Applications

One of the biggest challenges in machine learning is data collection. Training data is an important part since it determines how the model will behave. In object classification, capturing a large number of images per object and in different conditions is not always possible and can be very time-consuming and tedious. Accordingly, this work explores the creation of artificial images using a game engine to cope with limited data in the training dataset. We combine real and synthetic data to train the object classification engine, a strategy that has shown to be beneficial to increase confidence in the decisions made by the classifier, which is often critical in industrial setups. To combine real and synthetic data, we first train the classifier on a massive amount of synthetic data, and then we fine-tune it on real images. Another important result is that the amount of real images needed for fine-tuning is not very high, reaching top accuracy with just 12 or 24 images per class. This substantially reduces the requirements of capturing a great amount of real data.Comment: Accepted for publication at ICPRA

Class distribution-aware adaptive margins and cluster embedding for classification of fruit and vegetables at supermarket self-checkouts

The complex task of vision based fruit and vegetables classification at a supermarket self-checkout poses significant challenges. These challenges include the highly variable physical features of fruit and vegetables i.e. colour, texture shape and size which are dependent upon ripeness and storage conditions in a supermarket as well as general product variation. Supermarket environments are also significantly variable with respect to lighting conditions. Attempting to build an exhaustive dataset to capture all these variations, for example a dataset of a fruit consisting of all possible colour variations, is nearly impossible. Moreover, some fruit and vegetable classes have significant similar physical features e.g. the colour and texture of cabbage and lettuce. Current state-of-the-art classification techniques such as those based on Deep Convolutional Neural Networks (DCNNs) are highly prone to errors resulting from the inter-class similarities and intra-class variations of fruit and vegetable images. The deep features of highly variable classes can invade the features of neighbouring similar classes in a learned feature space of the DCNN, resulting in confused classification hyper-planes. To overcome these limitations of current classification techniques we have proposed a class distribution-aware adaptive margins approach with cluster embedding for classification of fruit and vegetables. We have tested the proposed technique for cluster-based feature embedding and classification effectiveness. It is observed that introduction of adaptive classification margins proportional to the class distribution can achieve significant improvements in clustering and classification effectiveness. The proposed technique is tested for both clustering and classification, and promising results have been obtained

Characterising the agriculture 4.0 landscape - Emerging trends, challenges and opportunities

ReviewInvestment in technological research is imperative to stimulate the development of sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and sensor networks, robotics, artificial intelligence, big data, cloud computing, etc. foster the transition towards the Agriculture 4.0 era. This fourth revolution is currently seen as a possible solution for improving agricultural growth, ensuring the future needs of the global population in a fair, resilient and sustainable way. In this context, this article aims at characterising the current Agriculture 4.0 landscape. Emerging trends were compiled using a semi-automated process by analysing relevant scientific publications published in the past ten years. Subsequently, a literature review focusing these trends was conducted, with a particular emphasis on their applications in real environments. From the results of the study, some challenges are discussed, as well as opportunities for future research. Finally, a high-level cloud-based IoT architecture is presented, serving as foundation for designing future smart agricultural systems. It is expected that this work will positively impact the research around Agriculture 4.0 systems, providing a clear characterisation of the concept along with guidelines to assist the actors in a successful transition towards the digitalisation of the sectorinfo:eu-repo/semantics/publishedVersio

Charging water load prediction with a multilayer perceptron for an efficient facility management and maintenance of thermal-energy-storage air-conditioning

This research addresses the challenges in Thermal-Energy-Storage-Air-Conditioning (TES-AC) systems by developing a machine learning model for predicting the necessary water volume for chilling. TES-AC technology, utilizing thermal energy storage tanks, offers substantial benefits such as reduced chiller operation, cost savings, and lower carbon emissions. However, determining the optimal chilled water volume poses challenges. The primary objective is to design a machine learning model leveraging Multilayer Perceptron (MLP) for predicting water load, incorporating input variables like weather forecasts, day of the week, and occupancy data. The study validates the impact of weather data on chilled water volume, demonstrating its efficacy in prediction. The MLP-based model is fine-tuned through hyperparameter optimization, achieving a remarkable accuracy of 93.4%. The model provides specific water volume ranges, minimizing errors and aiding facility managers in informed decision-making to minimize disruptions. Technical significance lies in the model's flexibility, allowing retraining for diverse TES-AC plants without requiring specific sensor inputs. This adaptability promotes widespread TES-AC adoption, contributing to environmentally friendly practices in building construction. The integration of the model into facility management software enhances predictive capabilities while offering common features, ensuring seamless incorporation into existing systems. The research aligns with Sustainable Development Goals, particularly Goals 11, 12, and 13, emphasizing sustainable cities, responsible consumption, and climate action. By focusing on technical problem-solving, addressing challenges, and emphasizing the social significance through Sustainable Development Goals, this research provides a comprehensive solution to enhance TES-AC efficiency, thereby contributing to greener and more sustainable urban environments

Charging water load prediction with a multilayer perceptron for an efficient facility management and maintenance of thermal-energy-storage air-conditioning

This research addresses the challenges in Thermal-Energy-Storage-Air-Conditioning (TES-AC) systems by developing a machine learning model for predicting the necessary water volume for chilling. TES-AC technology, utilizing thermal energy storage tanks, offers substantial benefits such as reduced chiller operation, cost savings, and lower carbon emissions. However, determining the optimal chilled water volume poses challenges. The primary objective is to design a machine learning model leveraging Multilayer Perceptron (MLP) for predicting water load, incorporating input variables like weather forecasts, day of the week, and occupancy data. The study validates the impact of weather data on chilled water volume, demonstrating its efficacy in prediction. The MLP-based model is fine-tuned through hyperparameter optimization, achieving a remarkable accuracy of 93.4%. The model provides specific water volume ranges, minimizing errors and aiding facility managers in informed decision-making to minimize disruptions. Technical significance lies in the model's flexibility, allowing retraining for diverse TES-AC plants without requiring specific sensor inputs. This adaptability promotes widespread TES-AC adoption, contributing to environmentally friendly practices in building construction. The integration of the model into facility management software enhances predictive capabilities while offering common features, ensuring seamless incorporation into existing systems. The research aligns with Sustainable Development Goals, particularly Goals 11, 12, and 13, emphasizing sustainable cities, responsible consumption, and climate action. By focusing on technical problem-solving, addressing challenges, and emphasizing the social significance through Sustainable Development Goals, this research provides a comprehensive solution to enhance TES-AC efficiency, thereby contributing to greener and more sustainable urban environments