Solution for remote real-time visual expertise of agricultural objects

ArticleIn recent years automated image and video analyses of plants and animals have become important techniques in Pre cision Agriculture for the detection of anomalies in development. Unlikely, machine learning (i.e., artificial neural networks, support vector machine, and other relevant techniques) are not always able to support decision making. Nevertheless, experts can use these techniques for developing more precise solutions and analysis approaches. It is labour - intensive and time - consuming for the experts to continuously visit the production sites to make direct on - site observations. Therefore, videos from the site n eed to be made available for remote viewing and analysis. In some cases it is also essential to monitor different parts of objects in agriculture and animal farming (e.g., bottom of the plants, stomach of the animal, etc.) which are difficult to access in standard recording procedures. One possible solution for the farmer is the use of a portable camera with real - streaming option r ather than a stationary camera. The aim of this paper is the proposition of a solution for real - time video streaming of agricultural objects (plants and/or animals) for remote expert evaluation and diagnosis. The proposed system is based on a Raspberry Pi 3, which is used to transfer the video from the attached camera to the YouTube streaming service. Users will be able to watch the video stream from the YouTube service on any device that has a web browser. Several cameras (USB, and Raspberry Pi camera) and video resolutions (from 480p till 1 , 080p) are compared and analysed, to find the best option, taking into account video quality, frame rates, and latency. Energy consumption of the whole system is evaluated and for the chosen solution it is 645 mA

Drone-Based Cattle Detection Using Deep Neural Networks

© 2021, Springer Nature Switzerland AG. Cattle form an important source of farming in many countries. In literature, several attempts have been conducted to detect farm animals for different applications and purposes. However, these approaches have been based on detecting animals from images captured from ground level and most approaches use traditional machine learning approaches for their automated detection. In this modern era, Drones facilitate accessing images in challenging environments and scanning large-scale areas with minimum time, which enables many new applications to be established. Considering the fact that drones typically are flown at high altitude to facilitate coverage of large areas within a short time, the captured object size tend to be small and hence this significantly challenges the possible use of traditional machine learning algorithms for object detection. This research proposes a novel methodology to detect cattle in farms established in desert areas using Deep Neural Networks. We propose to detect animals based on a ‘group-of-animals’ concept and associated features in which different group sizes and animal density distribution are used. Two state-of-the-art Convolutional Neural Network (CNN) architectures, SSD-500 and YOLO V-3, are effectively configured, trained and used for the purpose and their performance efficiencies are compared. The results demonstrate the capability of the two generated CNN models to detect groups-of-animals in which the highest accuracy recorded was when using SSD-500 giving a F-score of 0.93, accuracy of 0.89 and mAP rate of 84.7

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: a systematic review

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced

Identification of bean varieties according to color features using artificial neural network

A machine vision and a multilayer perceptron artificial neural network (MLP-ANN) were applied to identify bean varieties, based on color features. Ten varieties of beans, which were grown in Iran (Khomein1, KS21108, Khomein2, Sarab1, Khomein3, KS21409, Akhtar2, Sarab2, KS21205, and G11870) were collected. Six color features of the bean and six color features of the spots were extracted and used as input for MLP-ANN classifier. In this study, 1000 data sets were used, 70% for training, 15% for validating and 15% for testing. The results showed that the applied machine vision and neural network were able to classify bean varieties with 100% sensibility and specificity, except with Sarab1 with sensibilities of 100%, 73.3%, 60% for the training, validation and testing processes, respectively and KS21108 with specificities of 100%, 79% and 71%, respectively for the aforementioned processes. Considering total sensibilities of 100%, 97.33%, 96% and also specificities of 100%, 97.9% and 97.1% for training, validation and testing of beans, respectively, the ANN could be used as a effective tool for classification of bean varieties

Identification of bean varieties according to color features using artificial neural network

A machine vision and a multilayer perceptron artificial neural network (MLP-ANN) were applied to identify bean varieties, based on color features. Ten varieties of beans, which were grown in Iran (Khomein1, KS21108, Khomein2, Sarab1, Khomein3, KS21409, Akhtar2, Sarab2, KS21205, and G11870) were collected. Six color features of the bean and six color features of the spots were extracted and used as input for MLP-ANN classifier. In this study, 1000 data sets were used, 70% for training, 15% for validating and 15% for testing. The results showed that the applied machine vision and neural network were able to classify bean varieties with 100% sensibility and specificity, except with Sarab1 with sensibilities of 100%, 73.3%, 60% for the training, validation and testing processes, respectively and KS21108 with specificities of 100%, 79% and 71%, respectively for the aforementioned processes. Considering total sensibilities of 100%, 97.33%, 96% and also specificities of 100%, 97.9% and 97.1% for training, validation and testing of beans, respectively, the ANN could be used as a effective tool for classification of bean varieties