This paper explores the utilization of innovative technologies such as RGB cameras, drones, and computer vision algorithms, for monitoring pests in orchards, with a specific focus on detecting the Halyomorpha halys (H...
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This paper explores the utilization of innovative technologies such as RGB cameras, drones, and computer vision algorithms, for monitoring pests in orchards, with a specific focus on detecting the Halyomorpha halys (HH), commonly known as the "brown marmorated stink bug". The integration of drones and machine learning (ML) into integrated pest management shows promising potential for effectively combating HH infestations. However, challenges arise from relying on vision models solely trained using high-quality images from public datasets. To address this issue, we create an ad hoc dataset of on-site images mainly captured with the help of a drone as well as other devices. We initially conduct an in-depth analysis of the captured images, considering factors such as blurriness and brightness, to possibly improve the performance of the ML algorithms. Afterwards, we undertake the training and evaluation of diverse ML models using distinct approaches within the YOLO framework. We employ a range of metrics to compare their performance and ultimately achieve a satisfactory outcome. Through the optimization of ML models and the correction of image imperfections, we contribute to advancing automated decision-making processes in pest insect monitoring and management, specifically in HH monitoring.
In sensory evaluation, there have been many attempts to obtain responses from the autonomic nervous system (ANS) by analyzing heart rate, body temperature, and facial expressions. However, the methods involved tend to...
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In sensory evaluation, there have been many attempts to obtain responses from the autonomic nervous system (ANS) by analyzing heart rate, body temperature, and facial expressions. However, the methods involved tend to be intrusive, which interfere with the consumers' responses as they are more aware of the measurements. Furthermore, the existing methods to measure different ANS responses are not synchronized among them as they are measured independently. This paper discusses the development of an integrated camera system paired with an Android PC application to assess sensory evaluation and biometric responses simultaneously in the Cloud, such as heart rate, blood pressure, facial expressions, and skin-temperature changes using video and thermal images acquired by the integrated system and analyzed through computer vision algorithms written in Matlab (R), and FaceReader (TM). All results can be analyzed through customized codes for multivariate data analysis, based on principal component analysis and cluster analysis. Data collected can be also used for machine-learning modeling based on biometrics as inputs and self-reported data as targets. Based on previous studies using this integrated camera and analysis system, it has shown to be a reliable, accurate, and convenient technique to complement the traditional sensory analysis of both food and nonfood products to obtain more information from consumers and/or trained panelists.
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