precision livestock farming
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Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 73
Author(s):  
Kaidong Lei ◽  
Chao Zong ◽  
Ting Yang ◽  
Shanshan Peng ◽  
Pengfei Zhu ◽  
...  

In large-scale sow production, real-time detection and recognition of sows is a key step towards the application of precision livestock farming techniques. In the pig house, the overlap of railings, floors, and sows usually challenge the accuracy of sow target detection. In this paper, a non-contact machine vision method was used for sow targets perception in complex scenarios, and the number position of sows in the pen could be detected. Two multi-target sow detection and recognition models based on the deep learning algorithms of Mask-RCNN and UNet-Attention were developed, and the model parameters were tuned. A field experiment was carried out. The data-set obtained from the experiment was used for algorithm training and validation. It was found that the Mask-RCNN model showed a higher recognition rate than that of the UNet-Attention model, with a final recognition rate of 96.8% and complete object detection outlines. In the process of image segmentation, the area distribution of sows in the pens was analyzed. The position of the sow’s head in the pen and the pixel area value of the sow segmentation were analyzed. The feeding, drinking, and lying behaviors of the sow have been identified on the basis of image recognition. The results showed that the average daily lying time, standing time, feeding and drinking time of sows were 12.67 h(MSE 1.08), 11.33 h(MSE 1.08), 3.25 h(MSE 0.27) and 0.391 h(MSE 0.10), respectively. The proposed method in this paper could solve the problem of target perception of sows in complex scenes and would be a powerful tool for the recognition of sows.


animal ◽  
2022 ◽  
Vol 16 (1) ◽  
pp. 100429
Author(s):  
C. Aquilani ◽  
A. Confessore ◽  
R. Bozzi ◽  
F. Sirtori ◽  
C. Pugliese

Dairy ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 12-28
Author(s):  
Panagiotis Simitzis ◽  
Christos Tzanidakis ◽  
Ouranios Tzamaloukas ◽  
Evangelia Sossidou

Although the effects of human–dairy cattle interaction have been extensively examined, data concerning small ruminants are scarce. The present review article aims at highlighting the effects of management practices on the productivity, physiology and behaviour of dairy animals. In general, aversive handling is associated with a milk yield reduction and welfare impairment. Precision livestock farming systems have therefore been applied and have rapidly changed the management process with the introduction of technological and computer innovations that contribute to the minimization of animal disturbances, the promotion of good practices and the maintenance of cattle’s welfare status and milk production and farms’ sustainability and competitiveness at high levels. However, although dairy farmers acknowledge the advantages deriving from the application of precision livestock farming advancements, a reluctance concerning their regular application to small ruminants is observed, due to economic and cultural constraints and poor technological infrastructures. As a result, targeted intervention training programmes are also necessary in order to improve the efficacy and efficiency of handling, especially of small ruminants.


2021 ◽  
Author(s):  
Axiu MAO ◽  
Claire Giraudet ◽  
Kai LIU ◽  
Ines De Almeida Nolasco ◽  
Zhiqin Xie ◽  
...  

The annual global production of chickens exceeds 25 billion birds, and they are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an "iceberg indicator" of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotations, which is very labour-intensive and time-consuming. Thus, a novel light-VGG11 was developed to automatically identify chicken distress calls using recordings (3,363 distress calls and 1,973 natural barn sounds) collected on intensive chicken farms. The light-VGG11 was modified from VGG11 with a significantly smaller size in parameters (9.3 million vs 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e., precision (94.58%), recall (94.89%), F1-score (94.73%), and accuracy (95.07%), therefore more useful for model deployment in practice. To further improve the light-VGG11's performance, we investigated the impacts of different data augmentation techniques (i.e., time masking, frequency masking, mixed spectrograms of the same class, and Gaussian noise) and found that they could improve distress calls detection by up to 1.52%. In terms of precision livestock farming, our research opens new opportunities for developing technologies used to monitor the output of distress calls in large, commercial chicken flocks.


2021 ◽  
Vol 2 ◽  
Author(s):  
Yongliang Qiao ◽  
Cameron Clark ◽  
Sabrina Lomax ◽  
He Kong ◽  
Daobilige Su ◽  
...  

Individual cattle identification is a prerequisite and foundation for precision livestock farming. Existing methods for cattle identification require radio frequency or visual ear tags, all of which are prone to loss or damage. Here, we propose and implement a new unified deep learning approach to cattle identification using video analysis. The proposed deep learning framework is composed of a Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with a self-attention mechanism. More specifically, the Inception-V3 CNN was used to extract features from a cattle video dataset taken in a feedlot with rear-view. Extracted features were then fed to a BiLSTM layer to capture spatio-temporal information. Then, self-attention was employed to provide a different focus on the features captured by BiLSTM for the final step of cattle identification. We used a total of 363 rear-view videos from 50 cattle at three different times with an interval of 1 month between data collection periods. The proposed method achieved 93.3% identification accuracy using a 30-frame video length, which outperformed current state-of-the-art methods (Inception-V3, MLP, SimpleRNN, LSTM, and BiLSTM). Furthermore, two different attention schemes, namely, additive and multiplicative attention mechanisms were compared. Our results show that the additive attention mechanism achieved 93.3% accuracy and 91.0% recall, greater than multiplicative attention mechanism with 90.7% accuracy and 87.0% recall. Video length also impacted accuracy, with video sequence length up to 30-frames enhancing identification performance. Overall, our approach can capture key spatio-temporal features to improve cattle identification accuracy, enabling automated cattle identification for precision livestock farming.


2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 44-45
Author(s):  
Aline Remus ◽  
Candido Pomar ◽  
Daniel Warner

Abstract Precision livestock farming (PLF) involves the use of sensors that captures large amounts of real-time information at the building, herd or animal level, which are later processed to control the system. Data processing can be accomplished using mathematical models (MM), artificial intelligence algorithms (AI) or a combination of these and other methods. The choice of the method must be made according to the volume of data to be processed, its nature and the relationship between the available information and the desired control of the system. Several components of PLF such as precision nutrition, early disease detection, animal welfare among others may require sophisticated data processing methods. MM is today the preferred method to estimate nutrient requirements in the precision nutrition component of PLF. Conventional MM estimate average population responses using historical population information. Important limitations of these models are the assumption that all the individuals of the population have the same response to a given nutrient provision and that they have not been developed for real-time estimations using up-to-date available information. Therefore, MM have to be developed specifically for PLF and operate in real-time at individual or small group level, considering the between and within-animal variation. Growth patterns, nutrient utilization and behavior vary among animals and herds. There are opportunities to combine data-driven AI with knowledge-driven MM to control more complex PLF components. AI thrive in large complex datasets, where establishing connections can be otherwise difficult due to data complexity, volume and where flexibility is needed to process real-time data from individuals. In contrast, knowledge-driven MM can simplify complex biological systems based on well-established concepts and information. In both cases, PLF models must be flexible enough to consider changes over time for the same animal or herd, and among animals and herds, acknowledging the method limitation while using its strength.


2021 ◽  
Author(s):  
Jonas S. Gomes ◽  
José Maria N. David ◽  
Regina Braga ◽  
Wagner Arbex ◽  
Bryan Barbosa ◽  
...  

The use of sensors in the agricultural sector generates a large volume of heterogeneous data that must be processed, stored, and analyzed to support decisions. In addition, decisions taken in agriculture need to be traceable due to the diversity of data and devices present in different agricultural contexts. With provenance, we can trace and analyze data to improve future decisions and avoid the usefulness ones. This article presents the e-LivestockProv architecture, focusing on data provenance.


2021 ◽  
Author(s):  
Simón Marín Giraldo ◽  
Julian David Ramirez Lopera ◽  
Mauricio Toro ◽  
Andres Salazar Galeano

This work introduces some of the most widely usedcompression algorithms, and their relevance to the field oflivestock farming, which has been historically characterizedfor requiring menial and inefficient labor, introducingenvironmental. And also for lacking the scale andautomation that cutting edge technologies can provide. Bydoing this we will explain how this opens the door tolocations untouched by technology, and the generaladvantages, and possibilities that integrating patternrecognition models bring to the table. In addition, we willexplain the ins and outs of these compression algorithms,and our reasoning behind our decision to choose analgorithm to implement in our pattern recognition model.To solve this problem, Seam Carving, Image Scaling andRun-Length encoding were used. With them we compressedthe images an average of 17.5% of their original size in atime complexity of O(L*N*M). This research shows howyou can create an efficient compression algorithm for usagein PLF.


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