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180 results on '"Melvyn L. Smith"'

1. Towards Machine Vision for Insect Welfare Monitoring and Behavioural Insights

Author

Mark F. Hansen, Alphonsus Oparaeke, Ryan Gallagher, Amir Karimi, Fahim Tariq, and Melvyn L. Smith

Subjects
machine vision, deep learning, insect farming, black soldier fly, domestic crickets, sex classification, Veterinary medicine, SF600-1100
Abstract

Machine vision has demonstrated its usefulness in the livestock industry in terms of improving welfare in such areas as lameness detection and body condition scoring in dairy cattle. In this article, we present some promising results of applying state of the art object detection and classification techniques to insects, specifically Black Soldier Fly (BSF) and the domestic cricket, with the view of enabling automated processing for insect farming. We also present the low-cost “Insecto” Internet of Things (IoT) device, which provides environmental condition monitoring for temperature, humidity, CO2, air pressure, and volatile organic compound levels together with high resolution image capture. We show that we are able to accurately count and measure size of BSF larvae and also classify the sex of domestic crickets by detecting the presence of the ovipositor. These early results point to future work for enabling automation in the selection of desirable phenotypes for subsequent generations and for providing early alerts should environmental conditions deviate from desired values.

Published
2022
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2. Towards Facial Expression Recognition for On-Farm Welfare Assessment in Pigs

Author

Mark F. Hansen, Emma M. Baxter, Kenneth M. D. Rutherford, Agnieszka Futro, Melvyn L. Smith, and Lyndon N. Smith

Subjects
animal welfare, pigs, deep learning, computer vision, stress detection, facial expression recognition, Agriculture (General), S1-972
Abstract

Animal welfare is not only an ethically important consideration in good animal husbandry but can also have a significant effect on an animal’s productivity. The aim of this paper was to show that a reduction in animal welfare, in the form of increased stress, can be identified in pigs from frontal images of the animals. We trained a convolutional neural network (CNN) using a leave-one-out design and showed that it is able to discriminate between stressed and unstressed pigs with an accuracy of >90% in unseen animals. Grad-CAM was used to identify the animal regions used, and these supported those used in manual assessments such as the Pig Grimace Scale. This innovative work paves the way for further work examining both positive and negative welfare states with the aim of developing an automated system that can be used in precision livestock farming to improve animal welfare.

Published
2021
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28. The Photoface database.

Author

Stefanos Zafeiriou, Mark F. Hansen, Gary A. Atkinson, Vasileios Argyriou, Maria Petrou, Melvyn L. Smith, and Lyndon N. Smith

Published
2011
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41. Quantitative potato tuber phenotyping by 3D imaging

Author

Yonghuai Liu, Bo Li, Xiangming Xu, Jiangang Liu, Zexi Rao, Melvyn L. Smith, and Liping Jin

Subjects
Horticulture, Control and Systems Engineering, 3d image, Sample size determination, fungi, Visual assessment, food and beverages, Soil Science, Agronomy and Crop Science, Analysis method, Food Science, Mathematics
Abstract

The accurate phenotyping of the external quality attributes of potato tubers is important in potato breeding. Currently, the assessment of potato tuber shape, together with eye density and depth, are based on subjective naked eye visual evaluation. However, such a manual visual assessment makes it very difficult to reliably phenotype these and other important, more complicated, geometrical traits, such as shape uniformity. In this study, a 3D image analysis method has been developed for counting potato eyes and estimating eye depth based on an evaluation of the curvature of an acquired 3D point cloud. Six shape uniformity-related traits, together with their shape indices (SI), were measured for six potato varieties. These were collected from three field experiments designed initially to study the effects of variation in nitrogen (N), potassium (K) and compound fertilisers along with tuber mass, on all investigated external traits. We demonstrate that a 3D image analysis technique can estimate the number of potato eyes and their depth with a high degree of accuracy. In addition, three shape uniformity traits were identified as offering a better power discrimination between varieties. The preliminary experiment found potato tuber mass to significantly affect both the shape uniformity and eye count, while fertiliser treatments showed no effect on all traits except SI. However, further investigation with a larger sample size is required for confirmation.

Published
2021
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