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For commercial broiler production, about 20,000–30,000 birds are raised in each confined house, which has caused growing public concerns on animal welfare. Currently, daily evaluation of broiler wellbeing and growth is conducted manually, which is labor-intensive and subjectively subject to human error. Therefore, there is a need for an automatic tool to detect and analyze the behaviors of chickens and predict their welfare status. In this study, we developed a YOLOv5-CBAM-broiler model and tested its performance for detecting broilers on litter floor. The proposed model consisted of two parts: (1) basic YOLOv5 model for bird or broiler feature extraction and object detection; and (2) the convolutional block attention module (CBAM) to improve the feature extraction capability of the network and the problem of missed detection of occluded targets and small targets. A complex dataset of broiler chicken images at different ages, multiple pens and scenes (fresh litter versus reused litter) was constructed to evaluate the effectiveness of the new model. In addition, the model was compared to the Faster R-CNN, SSD, YOLOv3, EfficientDet and YOLOv5 models. The results demonstrate that the precision, recall, F1 score and an mAP@0.5 of the proposed method were 97.3%, 92.3%, 94.7%, and 96.5%, which were superior to the comparison models. In addition, comparing the detection effects in different scenes, the YOLOv5-CBAM model was still better than the comparison method. Overall, the proposed YOLOv5-CBAM-broiler model can achieve real-time accurate and fast target detection and provide technical support for the management and monitoring of birds in commercial broiler houses.

Agriculture plays a crucial role in development, especially in low-income countries where the sector is large in terms of both aggregate income and total labor force [...]

The frame of corn harvester is prone to vibration bending and torsional deformation due to the vibration caused by field road bumps and fluctuations. It poses a serious challenge to the reliability of machinery. Therefore it is critical to explore the vibration mechanism, and to identify the vibration states under different working conditions. To address the above problem, a vibration state identification method is proposed in this paper. An improved empirical mode decomposition (EMD) algorithm was used to decrease noise for signals of high noise and non-stationary vibration in the field. The support vector machine (SVM) model was used for identification of frame vibration states under different working conditions. The results showed that: (1) an improved EMD algorithm could effectively reduce noise interference and restore the effective information of the original signal. (2) based on improved EMD – SVM method identify the vibration states of the frame with the accuracy of 99.21%. (3) The corn ears in grain tank were not sensitive to low order vibration, but had an absorption effect on high order vibration. The proposed method has the potential to be applied for accurately identifying vibration state and improving frame safety.

Maize is susceptible to infect pest disease, and early disease detection is key to preventing the reduction of maize yields. The raw data used for plant disease detection are commonly RGB images and hyperspectral images (HSI). RGB images can be acquired rapidly and low-costly, but the detection accuracy is not satisfactory. On the contrary, using HSIs tends to obtain higher detection accuracy, but HSIs are difficult and high-cost to obtain in field. To overcome this contradiction, we have proposed the maize spectral recovery disease detection framework which includes two parts: the maize spectral recovery network based on the advanced hyperspectral recovery convolutional neural network (HSCNN+) and the maize disease detection network based on the convolutional neural network (CNN). Taking raw RGB data as input of the framework, the output reconstructed HSIs are used as input of disease detection network to achieve disease detection task. As a result, the detection accuracy obtained by using the low-cost raw RGB data almost as same as that obtained by using HSIs directly. The HSCNN+ is found to be fit to our spectral recovery model and the reconstruction fidelity was satisfactory. Experimental results demonstrate that the reconstructed HSIs efficiently improve detection accuracy compared with raw RGB image in tested scenarios, especially in complex environment scenario, for which the detection accuracy increases by 6.14%. The proposed framework has the advantages of fast, low cost and high detection precision. Moreover, the framework offers the possibility of real-time and precise field disease detection and can be applied in agricultural robots.

Semantic segmentation and instance segmentation based on deep learning play a significant role in intelligent dairy goat farming. However, these algorithms require a large amount of pixel-level dairy goat image annotations for model training. At present, users mainly use Labelme for pixel-level annotation of images, which makes it quite inefficient and time-consuming to obtain a high-quality annotation result. To reduce the annotation workload of dairy goat images, we propose a novel interactive segmentation model called UA-MHFF-DeepLabv3+, which employs layer-by-layer multi-head feature fusion (MHFF) and upsampling attention (UA) to improve the segmentation accuracy of the DeepLabv3+ on object boundaries and small objects. Experimental results show that our proposed model achieved state-of-the-art segmentation accuracy on the validation set of DGImgs compared with four previous state-of-the-art interactive segmentation models, and obtained 1.87 and 4.11 on mNoC@85 and mNoC@90, which are significantly lower than the best performance of the previous models of 3 and 5. Furthermore, to promote the implementation of our proposed algorithm, we design and develop a dairy goat image-annotation system named DGAnnotation for pixel-level annotation of dairy goat images. After the test, we found that it just takes 7.12 s to annotate a dairy goat instance with our developed DGAnnotation, which is five times faster than Labelme.

Obtaining animal regions and the relative position relationship of animals in the scene is conducive to further studying animal habits, which is of great significance for smart animal farming. However, the complex breeding environment still makes detection difficult. To address the problems of poor target segmentation effects and the weak generalization ability of existing semantic segmentation models in complex scenes, a semantic segmentation model based on an improved DeepLabV3+ network (Imp-DeepLabV3+) was proposed. Firstly, the backbone network of the DeepLabV3+ model was replaced by MobileNetV2 to enhance the feature extraction capability of the model. Then, the layer-by-layer feature fusion method was adopted in the Decoder stage to integrate high-level semantic feature information with low-level high-resolution feature information at multi-scale to achieve more precise up-sampling operation. Finally, the SENet module was further introduced into the network to enhance information interaction after feature fusion and improve the segmentation precision of the model under complex datasets. The experimental results demonstrate that the Imp-DeepLabV3+ model achieved a high pixel accuracy (PA) of 99.4%, a mean pixel accuracy (MPA) of 98.1%, and a mean intersection over union (MIoU) of 96.8%. Compared to the original DeepLabV3+ model, the segmentation performance of the improved model significantly improved. Moreover, the overall segmentation performance of the Imp-DeepLabV3+ model surpassed that of other commonly used semantic segmentation models, such as Fully Convolutional Networks (FCNs), Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP), and U-Net. Therefore, this study can be applied to the field of scene segmentation and is conducive to further analyzing individual information and promoting the development of intelligent animal farming.

The precision spray of liquid fertilizer and pesticide to plants is an important task for agricultural robots in precision agriculture. By reducing the amount of chemicals being sprayed, it brings in a more economic and eco-friendly solution compared to conventional non-discriminated spray. The prerequisite of precision spray is to detect and track each plant. Conventional detection or segmentation methods detect all plants in the image captured under the robotic platform, without knowing the ID of the plant. To spray pesticides to each plant exactly once, tracking of every plant is needed in addition to detection. In this paper, we present LettuceTrack, a novel Multiple Object Tracking (MOT) method to simultaneously detect and track lettuces. When the ID of each plant is obtained from the tracking method, the robot knows whether a plant has been sprayed before therefore it will only spray the plant that has not been sprayed. The proposed method adopts YOLO-V5 for detection of the lettuces, and a novel plant feature extraction and data association algorithms are introduced to effectively track all plants. The proposed method can recover the ID of a plant even if the plant moves out of the field of view of camera before, for which existing Multiple Object Tracking (MOT) methods usually fail and assign a new plant ID. Experiments are conducted to show the effectiveness of the proposed method, and a comparison with four state-of-the-art Multiple Object Tracking (MOT) methods is shown to prove the superior performance of the proposed method in the lettuce tracking application and its limitations. Though the proposed method is tested with lettuce, it can be potentially applied to other vegetables such as broccoli or sugar beat.

This paper proposes a method for automatic pig detection and segmentation using RGB-D data for precision livestock farming. The proposed method combines the enhanced YOLOv5s model with the Res2Net bottleneck structure, resulting in improved fine-grained feature extraction and ultimately enhancing the precision of pig detection and segmentation in 2D images. Additionally, the method facilitates the acquisition of 3D point cloud data of pigs in a simpler and more efficient way by using the pig mask obtained in 2D detection and segmentation and combining it with depth information. To evaluate the effectiveness of the proposed method, two datasets were constructed. The first dataset consists of 5400 images captured in various pig pens under diverse lighting conditions, while the second dataset was obtained from the UK. The experimental results demonstrated that the improved YOLOv5s_Res2Net achieved a mAP@0.5:0.95 of 89.6% and 84.8% for both pig detection and segmentation tasks on our dataset, while achieving a mAP@0.5:0.95 of 93.4% and 89.4% on the Edinburgh pig behaviour dataset. This approach provides valuable insights for improving pig management, conducting welfare assessments, and estimating weight accurately.

Grape downy mildew (GDM) disease is a common plant leaf disease, and it causes serious damage to grape production, reducing yield and fruit quality. Traditional manual disease detection relies on farm experts and is often time-consuming. Computer vision technologies and artificial intelligence could provide automatic disease detection for real-time controlling the spread of disease on the grapevine in precision viticulture. To achieve the best trade-off between GDM detection accuracy and speed under natural environments, a deep learning based approach named YOLOv5-CA is proposed in this study. Here coordinate attention (CA) mechanism is integrated into YOLOv5, which highlights the downy mildew disease-related visual features to enhance the detection performance. A challenging GDM dataset was acquired in a vineyard under a nature scene (consisting of different illuminations, shadows, and backgrounds) to test the proposed approach. Experimental results show that the proposed YOLOv5-CA achieved a detection precision of 85.59%, a recall of 83.70%, and a mAP@0.5 of 89.55%, which is superior to the popular methods, including Faster R-CNN, YOLOv3, and YOLOv5. Furthermore, our proposed approach with inference occurring at 58.82 frames per second, could be deployed for the real-time disease control requirement. In addition, the proposed YOLOv5-CA based approach could effectively capture leaf disease related visual features resulting in higher GDE detection accuracy. Overall, this study provides a favorable deep learning based approach for the rapid and accurate diagnosis of grape leaf diseases in the field of automatic disease detection.

Seagrasses are the only submerged marine higher plants, which can colonize the sea through sexual (via seeds) reproduction. The transition between seed dormancy and germination is an important ecological trait and a key stage in the life cycle of higher plants. According to our observations, the seeds of Zostera marina L. (eelgrass) in Swan Lake (SL) and Qingdao Bay (QB) in northern China have the same maturation time (summer) but different germination time. To investigate this phenomenon, we further carried out reciprocal transplantation experiment and transcriptome analysis. Results revealed that differences in the seed germination time between the two sites do exist and are determined by internal molecular mechanisms as opposed to environmental factors. Furthermore, we conducted comparative transcriptome analysis of seeds at the mature and early germination stages in both locations. The results that the number of genes related to energy, hormone and cell changes was higher in SL than in QB, could account for that the dormancy depth of seeds in SL was deeper than that in QB; consequently, the seeds in SL needed to mobilize more related genes to break dormancy and start germination. The results could have important practical implications for seagrass meadow restoration via seeds and provide in-depth and comprehensive data for understanding the molecular mechanisms related to seagrass seed germination.

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.

Achieving rapid and accurate detection of apple leaf diseases in the natural environment is essential for the growth of apple plants and the development of the apple industry. In recent years, deep learning has been widely studied and applied to apple leaf disease detection. However, existing networks have too many parameters to be easily deployed or lack research on leaf diseases in complex backgrounds to effectively use in real agricultural environments. This study proposes a novel deep learning network, YOLOX-ASSANano, which is an improved lightweight real-time model for apple leaf disease detection based on YOLOX-Nano. We improved the YOLOX-Nano backbone using a designed asymmetric ShuffleBlock, a CSP-SA module, and blueprint-separable convolution (BSConv), which significantly enhance feature-extraction capability and boost detection performance. In addition, we construct a multi-scene apple leaf disease dataset (MSALDD) for experiments. The experimental results show that the YOLOX-ASSANano model with only 0.83 MB parameters achieves 91.08% mAP on MSALDD and 58.85% mAP on the public dataset PlantDoc with a speed of 122 FPS. This study indicates that the YOLOX-ASSANano provides a feasible solution for the real-time diagnosis of apple leaf diseases in natural scenes, and could be helpful for the detection of other plant diseases.

Pork accounts for an important proportion of livestock products. For pig farming, a lot of manpower, material resources and time are required to monitor pig health and welfare. As the number of pigs in farming increases, the continued use of traditional monitoring methods may cause stress and harm to pigs and farmers and affect pig health and welfare as well as farming economic output. In addition, the application of artificial intelligence has become a core part of smart pig farming. The precision pig farming system uses sensors such as cameras and radio frequency identification to monitor biometric information such as pig sound and pig behavior in real-time and convert them into key indicators of pig health and welfare. By analyzing the key indicators, problems in pig health and welfare can be detected early, and timely intervention and treatment can be provided, which helps to improve the production and economic efficiency of pig farming. This paper studies more than 150 papers on precision pig farming and summarizes and evaluates the application of artificial intelligence technologies to pig detection, tracking, behavior recognition and sound recognition. Finally, we summarize and discuss the opportunities and challenges of precision pig farming.

Seagrass meadows are critical ecosystems, and they are among the most threatened habitats on the planet. As an anthropogenic biotic invader, Spartina alterniflora Loisel. competes with native plants, threatens native ecosystems and coastal aquaculture, and may cause local biodiversity to decline. The distribution area of the exotic species S. alterniflora in the Yellow River Delta had been expanding to ca.4,000 ha from 1990 to 2018. In this study, we reported, for the first time, the competitive effects of the exotic plant (S. alterniflora) on seagrass (Zostera japonica Asch. & Graebn.) by field investigation and a transplant experiment in the Yellow River Delta. Within the first 3 months of the field experiment, S. alterniflora had pushed forward 14 m into the Z. japonica distribution region. In the study region, the area of S. alterniflora in 2019 increased by 516 times compared with its initial area in 2015. Inhibition of Z. japonica growth increased with the invasion of S. alterniflora. Z. japonica had been degrading significantly under the pressure of S. alterniflora invasion. S. alterniflora propagates sexually via seeds for long distance invasion and asexually by tillers and rhizomes for short distance invasion. Our results describe the invasion pattern of S. alterniflora and can be used to develop strategies for prevention and control of S. alterniflora invasion.

Seagrasses play an important role in the geochemical cycling of elements in coastal ecosystems. Eelgrass (Zostera marina L.) is a marine foundation species essential for coastal ecosystem services in the temperate northern hemisphere. The elemental (C, N, and P) and isotope (δ13C and δ15N) contents of eelgrass tissues were measured at nine sampling sites across northern China. Trends at temporal and spatial scales were analyzed to examine temporal-spatial variations in seagrass characteristics, and relationships between different tissues were also analyzed to identify a valuable ecological indicator. Elemental contents (N and P) of eelgrass were variable and demonstrated marked seasonal variations, while the isotope content (δ13C and δ15N) demonstrated marked spatial variations. Elemental contents (N and P) in eelgrass tissues showed a clear annual trend with minima in summer and maxima in spring-winter in Swan Lake. Mean N contents (%) of leaf blades and leaf sheaths in Swan Lake were 2.56 ± 0.89 and 2.53 ± 0.96, respectively, and mean P contents of blades and sheaths were 0.31 ± 0.13 and 0.41 ± 0.17, respectively. N and P contents were higher in aboveground than in belowground tissues; due to C stability, C/N and C/P were lower in aboveground than in belowground tissues. Among different eelgrass populations, the ranges of δ15N isotope ratios in leaf blades and leaf sheaths were 5.57–9.64 and 5.89–9.79, respectively. Elemental (N and P) and isotope (δ13C and δ15N) contents of leaf sheaths were positively correlated with those of blades; this was evident regardless of the season and site, suggesting that the sheath elemental and stable isotope content could be a valuable indicator of that in leaf blades for seagrass ecosystem geochemical research.

Computer vision-based technologies play a key role in precision livestock farming, and video-based analysis approaches have been advocated as useful tools for automatic animal monitoring, behavior analysis, and efficient welfare measurement management. Accurately and efficiently segmenting animals’ contours from their backgrounds is a prerequisite for vision-based technologies. Deep learning-based segmentation methods have shown good performance through training models on a large amount of pixel-labeled images. However, it is challenging and time-consuming to label animal images due to their irregular contours and changing postures. In order to reduce the reliance on the number of labeled images, one-shot learning with a pseudo-labeling approach is proposed using only one labeled image frame to segment animals in videos. The proposed approach is mainly comprised of an Xception-based Fully Convolutional Neural Network (Xception-FCN) module and a pseudo-labeling (PL) module. Xception-FCN utilizes depth-wise separable convolutions to learn different-level visual features and localize dense prediction based on the one single labeled frame. Then, PL leverages the segmentation results of the Xception-FCN model to fine-tune the model, leading to performance boosts in cattle video segmentation. Systematic experiments were conducted on a challenging feedlot cattle video dataset acquired by the authors, and the proposed approach achieved a mean intersection-over-union score of 88.7% and a contour accuracy of 80.8%, outperforming state-of-the-art methods (OSVOS and OSMN). Our proposed one-shot learning approach could serve as an enabling component for livestock farming-related segmentation and detection applications.

The growing world population has increased the demand for animal-sourced protein. However, animal farming productivity is faced with challenges from traditional farming practices, socioeconomic status, and climate change. In recent years, smart sensors, big data, and deep learning have been applied to animal welfare measurement and livestock farming applications, including behaviour recognition and health monitoring. In order to facilitate research in this area, this review summarises and analyses some main techniques used in smart livestock farming, focusing on those related to cattle lameness detection and behaviour recognition. In this study, more than 100 relevant papers on cattle lameness detection and behaviour recognition have been evaluated and discussed. Based on a review and a comparison of recent technologies and methods, we anticipate that intelligent perception for cattle behaviour and welfare monitoring will develop towards standardisation, a larger scale, and intelligence, combined with Internet of things (IoT) and deep learning technologies. In addition, the key challenges and opportunities of future research are also highlighted and discussed.

Seagrass meadows play critical roles in supporting a high level of biodiversity but are continuously threatened by human activities, such as sea reclamation. In this study, we reported on a large seagrass (Zostera marina L.) meadow in Caofeidian shoal harbor in the Bohai Sea of northern China. We evaluated the environmental impact of sea reclamation activities using Landsat imagery (1974–2019) by mapping seagrass meadow distribution changes. ISODATA was adopted for the unsupervised classification and mapping of seagrass beds. The error matrix developed using the in situ data obtained from acoustic surveys for Landsat 8OLI image classification was 87.20% accurate. The maps showed rapidly increasing changes in seagrass meadows as the amount of reclaimed land increased. Some seagrass meadows experienced large-scale changes, and sea reclamation has been suggested as the main factor responsible for habitat loss, which results from physical damage, excessive sedimentation, and increased turbidity caused by reclamation. In addition, habitat degradation may have resulted from three storm surges induced by typhoons in 1992–1998. Fortunately, land reclamation, forming an artificial “longshore bar”, buffers seagrass meadows from wave actions, providing relatively sheltered conditions, which has allowed a large habitat increase since 2012. These were the largest eelgrass meadows (3,217.32 ha), with a peripheral area of ~100 km2, in the Bohai Sea of northern China in 2019. However, the existing largest eelgrass beds in China are threatened by trawling, clam harvesting (especially clam sucking), channel dredging, and culture pond construction. Our work will help coastal managers monitor the environmental impacts of reclamation activities on seagrass meadows on a large spatio-temporal scale and will also provide information for seagrass restoration using artificial “longshore bars”.

Convolutional Network (ConvNet), with its strong image representation ability, has achieved significant progress in the computer vision and robotic fields. In this paper, we propose a visual localization approach based on place recognition that combines the powerful ConvNet features and localized image sequence matching. The image distance matrix is constructed based on the cosine distance of extracted ConvNet features, and then a sequence search technique is applied on this distance matrix for the final visual recognition. To speed up the computational efficiency, the locality sensitive hashing (LSH) method is applied to achieve real-time performances with minimal accuracy degradation. We present extensive experiments on four real world data sets to evaluate each of the specific challenges in visual recognition. A comprehensive performance comparison of different ConvNet layers (each defining a level of features) considering both appearance and illumination changes is conducted. Compared with the traditional approaches based on hand-crafted features and single image matching, the proposed method shows good performances even in the presence of appearance and illumination changes.

Visual localization is widely used in autonomous navigation system and Advanced Driver Assistance Systems (ADAS). However, visual-based localization in seasonal changing situations is one of the most challenging topics in computer vision and the intelligent vehicle community. The difficulty of this task is related to the strong appearance changes that occur in scenes due to weather or season changes. In this paper, a place recognition based visual localization method is proposed, which realizes the localization by identifying previously visited places using the sequence matching method. It operates by matching query image sequences to an image database acquired previously (video acquired during traveling period). In this method, in order to improve matching accuracy, multi-feature is constructed by combining a global GIST descriptor and local binary feature CSLBP (Center-symmetric local binary patterns) to represent image sequence. Then, similarity measurement according to Chi-square distance is used for effective sequences matching. For experimental evaluation, the relationship between image sequence length and sequences matching performance is studied. To show its effectiveness, the proposed method is tested and evaluated in four seasons outdoor environments. The results have shown improved precision–recall performance against the state-of-the-art SeqSLAM algorithm.