As global demand for poultry products, environmental sustainability, and health consciousness rises with time, the poultry industry faces both substantial challenges and new opportunities. Therefore, this review paper provides a comprehensive overview of sustainable poultry farming, focusing on integrating genetic improvements, alternative feed, precision technologies, waste management, and biotechnological innovations. Together, these strategies aim to minimize ecological footprints, uphold ethical standards, improve economic feasibility, and enhance industry resilience. In addition, this review paper explores various sustainable strategies, including eco-conscious organic farming practices and innovative feed sources like insect-based proteins, single-cell proteins, algal supplements, and food waste utilization. It also addresses barriers to adoption, such as technical challenges, financial constraints, knowledge gaps, and policy frameworks, which are crucial for advancing the poultry industry. This paper examined organic poultry farming in detail, noting several benefits like reduced pesticide use and improved animal welfare. Additionally, it discusses optimizing feed efficiency, an alternate energy source (solar photovoltaic/thermal), effective waste management, and the importance of poultry welfare. Transformative strategies, such as holistic farming systems and integrated approaches, are proposed to improve resource use and nutrient cycling and promote climate-smart agricultural practices. The review underscores the need for a structured roadmap, education, and extension services through digital platforms and participatory learning to promote sustainable poultry farming for future generations. It emphasizes the need for collaboration and knowledge exchange among stakeholders and the crucial role of researchers, policymakers, and industry professionals in shaping a future where sustainable poultry practices lead the industry, committed to ethical and resilient poultry production.

Automated retinal vessel segmentation is among the most significant application and research topics in ophthalmologic image analysis. Deep learning based retinal vessel segmentation models have attracted much attention in the recent years. However, current deep network designs tend to predominantly focus on vessels which are easy to segment, while overlooking vessels which are more difficult to segment, such as thin vessels or those with uncertain boundaries. To address this critical gap, we propose a new end-to-end deep learning architecture for retinal vessel segmentation: hard attention net (HAnet). Our design is composed of three decoder networks: the first of which dynamically locates which image regions are “hard” or “easy” to analyze, while the other two aim to segment retinal vessels in these “hard” and “easy” regions independently. We introduce attention mechanisms in the network to reinforce focus on image features in the “hard” regions. Finally, a final vessel segmentation map is generated by fusing all decoder outputs. To quantify the network's performance, we evaluate our model on four public fundus photography datasets (DRIVE, STARE, CHASE_DB1, HRF), two recent published color scanning laser ophthalmoscopy image datasets (IOSTAR, RC-SLO), and a self-collected indocyanine green angiography dataset. Compared to existing state-of-the-art models, the proposed architecture achieves better/comparable performances in segmentation accuracy, area under the receiver operating characteristic curve (AUC), and f1-score. To further gauge the ability to generalize our model, cross-dataset and cross-modality evaluations are conducted, and demonstrate promising extendibility of our proposed network architecture.

Tomato spotted wilt virus is a wide-spread plant disease in the world. It can threaten thousands of plants with a persistent and propagative manner. Early disease detection is expected to be able to control the disease spread, to facilitate management practice, and further to guarantee accompanying economic benefits. Hyperspectral imaging, a powerful remote sensing tool, has been widely applied in different science fields, especially in plant science domain. Rich spectral information makes disease detection possible before visible disease symptoms showing up. In the paper, a new hyperspectral analysis proximal sensing method based on generative adversarial nets (GAN) is proposed, named as outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN). It is an all-in-one method, which integrates the tasks of plant segmentation, spectrum classification and image classification. The model focuses on image pixels, which can effectively visualize potential plant disease positions, and keep experts’ attention on these diseased pixels. Meanwhile, this new model can improve the performances of classic spectrum band selection methods, including the maximum variance principle component analysis (MVPCA), fast density-peak-based clustering, and similarity-based unsupervised band selection. Selecting spectrum wavebands reasonably is an important preprocessing step in spectroscopy/hyperspectral analysis applications, which can reduce the computation time for potential in-field applications, affect the prediction results and make the hyperspectral analysis results explainable. In the experiment, the hyperspectral reflectance imaging system covers the spectral range from 395 nm to 1005 nm. The proprosed model makes use of 83 bands to do the analysis. The plant level classification accuracy gets 96.25% before visible symptoms shows up. The pixel prediction false positive rate in healthy plants gets as low as 1.47%. Combining the OR-AC-GAN with three existing band selection algorithms, the performance of these band selection models can be significantly improved. Among them, MVPCA can leverage only 8 spectrum bands to get the same plant level classification accuracy as OR-AC-GAN, and the pixel prediction false positive rate in healthy plants is 1.57%, which is also comparable to OR-AC-GAN. This new model can be potentially transferred to other plant diseases detection applications. Its property to boost the performance of existing band selection methods can also accelerate the in-field applications of hyperspectral imaging technology.

Wood veneer defect detection plays a vital role in the wood veneer production industry. Studies on wood veneer defect detection usually focused on detection accuracy for industrial applications but ignored algorithm execution speed; thus, their methods do not meet the required speed of online detection. In this paper, a new detection method is proposed that achieves high accuracy and a suitable speed for online production. Firstly, 2838 wood veneer images were collected using data collection equipment developed in the laboratory and labeled by experienced workers from a wood company. Then, an integrated model, glance multiple channel mask region convolution neural network (R-CNN), was constructed to detect wood veneer defects, which included a glance network and a multiple channel mask R-CNN. Neural network architect search technology was used to automatically construct the glance network with the lowest number of floating-point operations to pick out potential defect images out of numerous original wood veneer images. A genetic algorithm was used to merge the intermediate features extracted by the glance network. Multi-Channel Mask R-CNN was then used to classify and locate the defects. The experimental results show that the proposed method achieves a 98.70% overall classification accuracy and a 95.31% mean average precision, and only 2.5 s was needed to detect a batch of 50 standard images and 50 defective images. Compared with other wood veneer defect detection methods, the proposed method is more accurate and faster.