Search Results (20)

The behavior of floor-raised chickens is closely linked to their health status and environmental comfort. As a type of broiler chicken with special behaviors, understanding the daily actions of yellow-feathered chickens is crucial for accurately checking their health and improving breeding practices. Addressing the challenges of high computational complexity and insufficient detection accuracy in existing floor-raised chicken behavior recognition models, a lightweight behavior recognition model was proposed for floor-raised yellow-feathered chickens, based on a Dual-Backbone Heterogeneous YOLO Network. Firstly, DualHet-YOLO enhances the feature extraction capability of floor-raised chicken images through a dual-path feature map extraction architecture and optimizes the localization and classification of multi-scale targets using a TriAxis Unified Detection Head. Secondly, a Proportional Scale IoU loss function is introduced that improves regression accuracy. Finally, a lightweight structure Eff-HetKConv was designed, significantly reducing model parameters and computational complexity. Experiments on a private floor-raised chicken behavior dataset show that, compared with the baseline YOLOv11 model, the DualHet-YOLO model increases the mAP for recognizing five behaviors—pecking, resting, walking, dead, and inactive—from 77.5% to 84.1%. Meanwhile, it reduces model parameters by 14.6% and computational complexity by 29.2%, achieving a synergistic optimization of accuracy and efficiency. This approach provides an effective solution for lightweight object detection in poultry behavior recognition. Full article

With the increasing urgency for digital transformation in large-scale caged layer farms, traditional methods for monitoring the environment and chicken health, which often rely on human experience, face challenges related to low efficiency and poor real-time performance. In this study, we focused on caged layer chickens and proposed an improved abnormal beak detection model based on the You Only Look Once v8 (YOLOv8) framework. Data collection was conducted using an inspection robot, enhancing automation and consistency. To address the interference caused by chicken cages, an Efficient Multi-Scale Attention (EMA) mechanism was integrated into the Spatial Pyramid Pooling-Fast (SPPF) module within the backbone network, significantly improving the model’s ability to capture fine-grained beak features. Additionally, the standard convolutional blocks in the neck of the original model were replaced with Grouped Shuffle Convolution (GSConv) modules, effectively reducing information loss during feature extraction. The model was deployed on edge computing devices for the real-time detection of abnormal beak features in layer chickens. Beyond local detection, a digital twin remote monitoring system was developed, combining three-dimensional (3D) modeling, the Internet of Things (IoT), and cloud-edge collaboration to create a dynamic, real-time mapping of physical layer farms to their virtual counterparts. This innovative approach not only improves the extraction of subtle features but also addresses occlusion challenges commonly encountered in small target detection. Experimental results demonstrate that the improved model achieved a detection accuracy of 92.7%. In terms of the comprehensive evaluation metric (mAP), it surpassed the baseline model and YOLOv5 by 2.4% and 3.2%, respectively. The digital twin system also proved stable in real-world scenarios, effectively mapping physical conditions to virtual environments. Overall, this study integrates deep learning and digital twin technology into a smart farming system, presenting a novel solution for the digital transformation of poultry farming. Full article

The existence of dead broilers in flat broiler houses poses significant challenges to large-scale and welfare-oriented broiler breeding. To ensure the timely identification and removal of dead broilers, a mobile device based on visual technology for grasping them was meticulously designed in this study. Among the multiple recognition models explored, the YOLOv6 model was selected due to its exceptional performance, attaining an impressive 86.1% accuracy in identification. This model, when integrated with a specially designed robotic arm, forms a potent combination for effectively handling the task of grasping dead broilers. Extensive experiments were conducted to validate the efficacy of the device. The results reveal that the device achieved an average grasping rate of dead broilers of 81.3%. These findings indicate that the proposed device holds great potential for practical field deployment, offering a reliable solution for the prompt identification and grasping of dead broilers, thereby enhancing the overall management and welfare of broiler populations. Full article

Udder conformation traits interact with cow milk yield, and it is essential to study the udder characteristics at different levels of production to predict milk yield for managing cows on farms. This study aims to develop an effective method based on instance segmentation and an improved neural network to divide cow production groups according to udders of high- and low-yielding cows. Firstly, the SOLOv2 (Segmenting Objects by LOcations) method was utilized to finely segment the cow udders. Secondly, feature extraction and data processing were conducted to define several cow udder features. Finally, the improved CNN-LSTM (Convolution Neural Network-Long Short-Term Memory) neural network was adopted to classify high- and low-yielding udders. The research compared the improved CNN-LSTM model and the other five classifiers, and the results show that CNN-LSTM achieved an overall accuracy of 96.44%. The proposed method indicates that the SOLOv2 and CNN-LSTM methods combined with analysis of udder traits have the potential for assigning cows to different production groups. Full article

The heat stress response of broilers will adversely affect the large-scale and welfare of the breeding of broilers. In order to detect the heat stress state of broilers in time, make reasonable adjustments, and reduce losses, this paper proposed an improved Cascade R-CNN (Region-based Convolutional Neural Networks) model based on visual technology to identify the behavior of yellow-feathered broilers. The improvement of the model solved the problem of the behavior recognition not being accurate enough when broilers were gathered. The influence of different iterations on the model recognition effect was compared, and the optimal model was selected. The final average accuracy reached 88.4%. The behavioral image data with temperature and humidity data were combined, and the heat stress evaluation model was optimized using the PLSR (partial least squares regression) method. The behavior recognition results and optimization equations were verified, and the test accuracy reached 85.8%. This proves the feasibility of the heat stress evaluation optimization equation, which can be used for reasonably regulating the broiler chamber. Full article

NH₃ emissions are an environmental issue that is of wide concern in livestock production. In intensive livestock farming, it is necessary to study outdoor ammonia concentrations under various conditions to maximize the protection of livestock caretakers’ health in and around the facilities. In this study, the ammonia concentrations outside smart broiler chambers in 60 scenarios, with conditions including 4 broiler chamber densities, 3 wind directions, and 5 outlet emission intensities, were simulated based on computational fluid dynamics (CFD) technology. The results show that (1) outdoor ammonia tends to accumulate near the outlet when the wind direction angle is small, while it has a wider range of influence when the angle is vertical; (2) building a smart broiler chamber cluster for intensive livestock farming is environmentally friendly; and (3) keeping the ammonia outlet perpendicular to the local dominant wind direction can effectively prevent high concentrations of ammonia around the chambers. In practical applications, the conclusions of this study can be used to arrange the layout and direction of smart broiler chamber clusters. Full article

Rice seed variety purity, an important index for measuring rice seed quality, has a great impact on the germination rate, yield, and quality of the final agricultural products. To classify rice varieties more efficiently and accurately, this study proposes a multimodal l fusion detection method based on an improved voting method. The experiment collected eight common rice seed types. Raytrix light field cameras were used to collect 2D images and 3D point cloud datasets, with a total of 3194 samples. The training and test sets were divided according to an 8:2 ratio. The experiment improved the traditional voting method. First, multiple models were used to predict the rice seed varieties. Then, the predicted probabilities were used as the late fusion input data. Next, a comprehensive score vector was calculated based on the performance of different models. In late fusion, the predicted probabilities from 2D and 3D were jointly weighted to obtain the final predicted probability. Finally, the predicted value with the highest probability was selected as the final value. In the experimental results, after late fusion of the predicted probabilities, the average accuracy rate reached 97.4%. Compared with the single support vector machine (SVM), k-nearest neighbors (kNN), convolutional neural network (CNN), MobileNet, and PointNet, the accuracy rates increased by 4.9%, 8.3%, 18.1%, 8.3%, and 9%, respectively. Among the eight varieties, the recognition accuracy of two rice varieties, Hannuo35 and Yuanhan35, by applying the voting method improved most significantly, from 73.9% and 77.7% in two dimensions to 92.4% and 96.3%, respectively. Thus, the improved voting method can combine the advantages of different data modalities and significantly improve the final prediction results. Full article

Sensors have been used in various agricultural production scenarios due to significant advances in the Agricultural Internet of Things (Ag-IoT), leading to smart agriculture. Intelligent control or monitoring systems rely heavily on trustworthy sensor systems. Nonetheless, sensor failures are likely due to various factors, including key equipment malfunction or human error. A faulty sensor can produce corrupted measurements, resulting in incorrect decisions. Early detection of potential faults is crucial, and fault diagnosis techniques have been proposed. The purpose of sensor fault diagnosis is to detect faulty data in the sensor and recover or isolate the faulty sensors so that the sensor can finally provide correct data to the user. Current fault diagnosis technologies are based mainly on statistical models, artificial intelligence, deep learning, etc. The further development of fault diagnosis technology is also conducive to reducing the loss caused by sensor failures. Full article

As a non-destructive detection method, an electronic nose can be used to assess the freshness of meats by collecting and analyzing their odor information. Deep learning can automatically extract features and uncover potential patterns in data, minimizing the influence of subjective factors such as selecting features artificially. A transfer-learning-based model was proposed for the electronic nose to detect the freshness of chicken breasts in this study. First, a 3D-printed electronic nose system is used to collect the odor data from chicken breast samples stored at 4 °C for 1–7 d. Then, three conversion to images methods are used to feed the recorded time series data into the convolutional neural network. Finally, the pre-trained AlexNet, GoogLeNet, and ResNet models are retrained in the last three layers while being compared to classic machine learning methods such as K Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machines (SVM). The final accuracy of ResNet is 99.70%, which is higher than the 94.33% correct rate of the popular machine learning model SVM. Therefore, the electronic nose combined with conversion to images shows great potential for using deep transfer learning methods for chicken freshness classification. Full article

The digging depth is an important factor affecting the mechanized garlic harvesting quality. At present, the digging depth of the garlic combine harvester (GCH) is adjusted manually, which leads to disadvantages such as slow response, poor accuracy, and being very dependent on the operator’s experience. To solve this problem, this paper proposes a machine vision-based automatic digging depth control system for the original garlic digging device. The system uses the improved YOLOv5 algorithm to calculate the length of the garlic root at the front end of the clamping conveyor chain in real-time, and the calculation result is sent back to the system as feedback. Then, the STM32 microcontroller is used to control the digging depth by expanding and contracting the electric putter of the garlic digging device. The experimental results of the presented control system show that the detection time of the system is 30.4 ms, the average accuracy of detection is 99.1%, and the space occupied by the model deployment is 11.4 MB, which suits the design of the real-time detection of the system. Moreover, the length of the excavated garlic roots is shorter than that of the system before modification, which represents a lower energy consumption of the system and a lower rate of impurities in harvesting, and the modified system is automatically controlled, reducing the operator’s workload. Full article

Accurate prediction of PM_2.5 concentration is one of the key tasks of air pollution assessment, early warning, and treatment. In this paper, four monitoring sites were arranged in Jiangbei New District of Nanjing City, China. The environmental parameters such as PM_2.5/PM₁₀ concentration, temperature, and humidity were monitored from January to February 2020. A gated recurrent unit (GRU) network based on the PM_2.5 concentration prediction model was established to predict PM_2.5 concentration. The mean relative error (MRE), root mean square error (RMSE), and Pearson correlation coefficient were selected as the evaluation criteria for the accuracy of the GRU model. The data set was divided into a training set, a test set and a validation set at a ratio of 7:2:1, and the GRU model was used to predict the hourly value of PM_2.5 concentration in the next week. The prediction results show that the Pearson correlation coefficients between the predicted values and the monitored values of the four monitoring sites have reached more than 0.9, reflecting a strong correlation. The relative average errors are around 10%. The GRU model prediction of NJAU (Nanjing Agricultural University)-Pukou Campus Site is the most accurate, and the correlation coefficient, MRE, and RMSE are 0.970, 7.85%, and 9.6049, respectively, reflecting the good prediction performance of the model. Therefore, this research supports the prediction of air quality in different cities and regions, so people can take protective measures in advance and reduce the damage caused by air pollution to human bodies. Full article

The air environment (e.g., high concentration of carbon dioxide) in a pig house will affect the health conditions and growth performance of the pigs, and the quality of pork as well. In order to reduce the cumulative concentration of carbon dioxide in the pig house, the prediction model was established by the deep learning method to predict the changes of the carbon dioxide cumulative concentration in a pig house. This model will also be used for the real-time monitoring and adjustment of the concentration of carbon dioxide of the pig house. The experiment was designed to collect environmental parameters (e.g., temperature, humidity, wind speed, and carbon dioxide concentration) data in the pig house for several months. The ensemble empirical mode decomposition–gated recurrent unit (EEMD–GRU) prediction model was established in the prediction of carbon dioxide concentration in the pig house. The results show that compared with the other models, the prediction accuracy of the EEMD–GRU model is the highest, and the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and r-squared (R²) of carbon dioxide concentration in autumn and winter are 123.2 ppm, 88.3 ppm, 3.2%, and 0.99, respectively. The RMSE, MAE, MAPE, and R² for carbon dioxide concentration are 129.1 ppm, 93.2 ppm, 5.9%, and 0.76 in spring and summer. The prediction model proposed in this paper can effectively predict the concentration of carbon dioxide in the pig house and provide effective help for the precise control of the pig house environment. Full article

Apples are one of the most widely planted fruits in the world, with an extremely high annual production. Several issues should be addressed to avoid the damaging of samples during the quality grading process of apples (e.g., the long detection period and the inability to detect the internal quality of apples). In this study, an electronic nose (e-nose) detection system for apple quality grading based on the K-nearest neighbor support vector machine (KNN-SVM) was designed, and the nasal cavity structure of the e-nose was optimized by computational fluid dynamics (CFD) simulation. A KNN-SVM classifier was also proposed to overcome the shortcomings of the traditional SVMs. The performance of the developed device was experimentally verified in the following steps. The apples were divided into three groups according to their external and internal quality. The e-nose data were pre-processed before features extraction, and then Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to reduce the dimension of the datasets. The recognition accuracy of the PCA–KNN-SVM classifier was 96.45%, and the LDA–KNN-SVM classifier achieved 97.78%. Compared with other commonly used classifiers, (traditional KNN, SVM, Decision Tree, and Random Forest), KNN-SVM is more efficient in terms of training time and accuracy of classification. Generally, the apple grading system can be used to evaluate the quality of apples during storage. Full article

Heavy broilers exposed to hot summer conditions experience fluctuations in surface temperatures due to heat stress, which leads to decreased performance. Maintaining a bird’s homeostasis depends on several environmental factors (temperature, relative humidity, and air velocity). It is important to understand the responses of birds to environmental factors and the amount of heat loss to the surrounding environment to create thermal comfort for the heavy broilers for improved performances and welfare. This study investigates the variation in surface temperatures of heavy broilers under high and low air velocity treatments. Daytime, age and bird location’s effect on the surface temperature variation was also examined. The experiment was carried out in the poultry engineering laboratory of North Carolina State University during summers of 2017, 2018, and 2019 as a part of a comprehensive study on the effectiveness of wind chill application to mitigate heat stress on heavy broilers. This live broiler heat stress experiment was conducted under two dynamic air velocity treatments (high and low) with three chambers per treatment and 44 birds per chamber. Surface temperatures of the birds were recorded periodically through the experimental treatment cycles (flocks, 35–61 d) with infrared thermography in the morning, noon, evening, and nighttime. The overall mean surface temperature of the broilers under two treatments was found to be 35.89 ± 2.37 °C. The variation in surface temperature happened due to air temperature, thermal index, air velocity, bird’s age, daytime, and position of birds inside the experimental chambers. The surface temperatures were found lower under high air velocity treatment and higher under low air velocity treatment. During the afternoon time, the broilers’ surface temperatures were higher than other times of the day. It was also found that the birds’ surface temperature increased with age and temperature humidity indices. Based upon the experimental data of five flocks, a simple linear regression model was developed to predict surface temperature from the birds’ age, thermal indices, and air velocity. It will help assess heavy broilers’ thermal comfort under heat stress, which is essential to provide a comfortable environment for them. Full article