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• We introduce a unique dataset of UAV thermal imagery, meticulously collected under diverse heights, angles, and climatic conditions, designed to challenge and validate thermal imaging analysis. • An approach for tracking mammalian herbivores in thermal imagery using the Multi-Object Tracking and Segmentation model Track R-CNN is proposed. • 3D convolutions outperform LSTM convolutions as a temporal component. • The association head outperforms optical flow as a tracking mechanism. • The proposed approach is robust across a wide range of lighting and thermal conditions and on imagery collected at various heights and angles. Lightweight Unmanned Aerial Vehicles (UAVs) are emerging as a remote sensing survey tool for animal monitoring in several fields, such as precision livestock farming. Together with state-of-the-art computer vision techniques, UAV technology has drastically escalated our ability to acquire and analyse visual data in the field, lowering both costs and complications associated with collection and analysis. This paper addresses monitoring mammalian herbivores using the unexploited field of thermal Multi-Object Tracking and Segmentation (MOTS) in UAV imagery. In our research, a state-of-the-art MOTS algorithm (Track R-CNN) was trained and evaluated in the segmentation, detection and tracking of dairy cattle. Data collection was carried out in two farms with a UAV carrying a thermal camera at various angles and heights, and under different light (overcast/sunny) and thermal (16.5 °C range) conditions. Our findings suggest that dataset diversity and balance, especially regarding the range of conditions under which the data was collected, can significantly enhance tracking efficiency in specific scenarios. For training the algorithm, transfer learning was used as a knowledge migration method. The performance of our best model (68.5 sMOTSA, 79.6 MOTSA, 41 IDS, 100 % counting accuracy, and 87.2 MOTSP), which utilizes 3D convolutions and an association head, demonstrates the applicability and optimal performance of Track R-CNN in detecting, tracking, and counting herbivores in UAV thermal imagery under heterogenous conditions. Our findings demonstrate that 3D convolutions outperform Long-short Term Memory (LSTM) convolutions. However, LSTM convolutions also show optimal performance, offering a viable alternative. Furthermore, our results highlight the inability of Optical Flow to track motionless animals (-15 sMOTSA, −4.1 MOTSA and 2076 IDS) and the proficiency of the association head in differentiating static animals from the background. This research contributes to the growing body of knowledge in automated mammalian herbivore monitoring, with potential applications such as precision livestock farming and wildlife conservation. [ABSTRACT FROM AUTHOR]

• Amaranthus palmeri growth stage recognition was possible with YOLO architectures. • YOLO v8-X achieved the highest mAP@[0.5:0.95] for growth stage recognition of 47.34% • A. palmeri single-class outperformed 8-class recognition by over 20% (v7-original) • The first high-quality, benchmarking, growth stage dataset is provided. Many advanced image-based precision agricultural technologies for plant breeding, field crop research, and site-specific crop management hinge on the reliable detection and phenotyping of plants across highly variable morphological growth stages. Convolutional neural networks (CNNs) have shown promise for image-based plant phenotyping and weed recognition, but the ability to recognize growth stages, often with stark differences in appearance, is uncertain. Palmer amaranth (Amaranthus palmeri) is a particularly challenging weed plant in cotton (Gossypium hirsutum) production; due to high genetic diversity, it exhibits highly variable plant morphology across growth stages over a growing season, as well as between plants at a given growth stage. This paper investigates eight-class growth stage recognition of A. palmeri in cotton as a challenging detection case study for You Only Look Once (YOLO) architectures. In total, 26 architecture variants from YOLO v3, v5, v6, v6 3.0, v7, and v8 are compared on an eight-class growth stage dataset of A. palmeri. The highest mean average precision (mAP@[0.5:0.95]) for recognition of all growth stage classes was 47.34% achieved by v8-X, with inter-class confusion across visually similar classes. With all growth stages grouped as a single class, performance increased, with a maximum mAP@[0.5:0.95] of 67.05% achieved by v7-Original. Single-class recall of up to 81.42% was achieved by v5-X, and precision of up to 89.72% was achieved by v8-X. Class activation maps (CAM) were used to understand model attention on the complex dataset. Fewer classes, grouped by visual or size features improved performance over the ground-truth eight-class dataset. Successful growth stage detection highlights the substantial opportunity for improving plant phenotyping and weed recognition technologies with open-source object detection architectures. Additionally, the first open-access, benchmarking growth stage dataset ' Palmer amaranth growth stages – 8 (PAGS8) ' is presented, which is available here: https://weed-ai.sydney.edu.au/datasets/5c78d067-8750-4803-9cbe-57df8fae55e4. [ABSTRACT FROM AUTHOR]

[Display omitted] • We developed AICropCAM, an edge-computing enabled camera system for crop monitoring. • It integrated Raspberry Pi and Arduino for image processing and LoRa communication. • It ran a stack of four deep neural networks to characterize multiple plant/canopy parameters. • We quantified the power consumption and speed of the system for edge image-processing. • AICropCAM is a next-generation enabling technology for real-time, low-latency Ag applications. Precision Agriculture (PA) promises to meet the future demands for food, feed, fiber, and fuel while keeping their production sustainable and environmentally friendly. PA relies heavily on sensing technologies to inform site-specific decision supports for planting, irrigation, fertilization, spraying, and harvesting. Traditional point-based sensors enjoy small data sizes but are limited in their capacity to measure plant and canopy parameters. On the other hand, imaging sensors can be powerful in measuring a wide range of these parameters, especially when coupled with Artificial Intelligence. The challenge, however, is the lack of computing, electric power, and connectivity infrastructure in agricultural fields, preventing the full utilization of imaging sensors. This paper reported AICropCAM, a field-deployable imaging framework that integrated edge image processing, Internet of Things (IoT), and LoRaWAN for low-power, long-range communication. The core component of AICropCAM is a stack of four Deep Convolutional Neural Networks (DCNN) models running sequentially: CropClassiNet for crop type classification, CanopySegNet for canopy cover quantification, PlantCountNet for plant and weed counting, and InsectNet for insect identification. These DCNN models were trained and tested with >43,000 field crop images collected offline. AICropCAM was embodied on a distributed wireless sensor network with its sensor node consisting of an RGB camera for image acquisition, a Raspberry Pi 4B single-board computer for edge image processing, and an Arduino MKR1310 for LoRa communication and power management. Our testing showed that the time to run the DCNN models ranged from 0.20 s for InsectNet to 20.20 s for CanopySegNet, and power consumption ranged from 3.68 W for InsectNet to 5.83 W for CanopySegNet. The classification model CropClassiNet reported 94.5 % accuracy, and the segmentation model CanopySegNet reported 92.83 % accuracy. The two object detection models PlantCountNet and InsectNet reported mean average precision of 0.69 and 0.02 for the test images. Predictions from the DCNN models were transmitted to the ThingSpeak IoT platform for visualization and analytics. We concluded that AICropCAM successfully implemented image processing on the edge, drastically reduced the amount of data being transmitted, and could satisfy the real-time need for decision-making in PA. AICropCAM can be deployed on moving platforms such as center pivots or drones to increase its spatial coverage and resolution to support crop monitoring and field operations. [ABSTRACT FROM AUTHOR]

• A convolutional neural network for detecting uneaten feed is proposed. • Attention mechanism and small object detection layer are integrated into the network. • Lightweight modules are applied to improve detection speed without loss of accuracy. • The proposed algorithm can detect residual particles accurately in real-time. In order to increase feed utilization and reduce rearing costs, it is crucial to accurately detect remaining pellets in real-time and optimize feeding mechanisms for precise feeding in intensive aquaculture. Machine vision and acoustic methods, which are widely used for the detection of residual particles, fail to adequately address scenarios such as the detection of overlapping, conjoined, faraway and dense particles, as well as particles with interference from water surface ripples and fish motion. To address these issues, this paper proposed a model for uneaten feed detection based on You Only Look Once (YOLO)-v5 network, which was built as follows: (1) The last C3 module of the backbone network is introduced into the coordinated attention (CA) mechanism, which evolves into a coordinated feature attention extraction (CFAE) module to obtain global information. (2) The GSConv module, and the VoVGSCSP module which was the aggregative mode of GSConv module, were adopted in the neck network part. (3) The shallow feature map was spliced with the deep feature map, and the tiny target detection layer (DL) was added for detection. The model elucidates the true condition of the detected residual particles by enhancing detection accuracy and minimizing model complexity, enabling precise and rapid identification of the pellet. This article employs seven sets of data to conduct a comparative analysis of the outcomes achieved before and after enhancing the three major components of the network. The experimental results showed that the YOLOv5s-CAGSDL model achieved an accuracy of 94.1% for feed particles detection, effectively solving the above problems in the actual rearing environment. Meanwhile, compared with the YOLOv5s model, AP 50 and FPS of the YOLOv5s-CAGSDL model were increased by 6.30% and 10.80% respectively, bringing potential environmental and economic benefits in the actual aquaculture environment. The source codes and datasets are available at: https://github.com/AAAArmstrong/YOLOv5s-CAGSDL. [ABSTRACT FROM AUTHOR]

• Computer vision technology can identify and classify fish with different feeding status. • In the study of fish feeding behavior, the method of combining active learning and convolutional neural network can achieve higher accuracy while reducing the number of labeled samples. • The Label-score proposed in this paper can comprehensively evaluate the accuracy and the number of labeled samples. • Automatic classification of different feeding status of fish in aquaculture. The image-based fish feeding status analysis has the potential to bring the aquaculture industry a more intelligent and precise feeding process. However, most of this type of research currently is predicated on a fully supervised approach that not only requires experienced experts for sample labeling, but also the process appears to be time consuming and resource intensive. This makes it very difficult to obtain labeled samples. To solve this problem, we proposed a model method that combines the VGG16 network with active learning to reduce the number of labeled samples needed in the analysis of fish feeding status. The algorithm is composed of two modules: feature extraction classifier and information extractor. In addition, multiple training strategies such as Mish activation function and RAdam optimizer were used in the feature extraction classifier to improve the classification performance. Optimal selection of information entropy method was used to obtain the most informative samples. To evaluate the effectiveness of our method, we designed a fish feeding experiment and verified the performance of the proposed algorithm with a special focus on the accuracy and the number of labeled samples required. The results showed that the proposed algorithm has achieved satisfying results in accuracy (the test accuracy is 98%) and the number of labeled samples (approximately 1/10 of the original dataset). Therefore, the method proposed in this paper can be applied to aquaculture production to provide a reference for farmers' feeding operations. [ABSTRACT FROM AUTHOR]