Papers
6
Total Citations
743
H-Index
6
About
Dihua Wu is a leading researcher at the intersection of deep learning and precision agriculture, with a primary focus on intelligent monitoring and automation in horticulture and livestock farming. His most impactful contribution is the development of a channel-pruning-based YOLO v4 algorithm for the real-time detection of apple flowers in natural environments, a work that has garnered 493 citations and revolutionized automated blossom thinning and yield estimation. Wu has also made significant strides in animal husbandry, including the use of an improved ResNet-50 for non-invasive chicken gender identification (63 citations) and a computer vision system for monitoring the respiratory behavior of multiple cows (44 citations), addressing the critical challenge of occlusion in crowded farm settings. His comprehensive review on information perception in modern poultry farming (115 citations) synthesizes advances in sensor and vision technologies for the industry. More recently, Wu has extended his expertise to robotic systems, designing a deep-learning-driven peach packaging robot, and to biosecurity with DCDNet, a neural network for dead chicken detection in layer farms. His work consistently demonstrates how deep learning can automate labor-intensive tasks, enhance animal welfare, and improve agricultural efficiency.
Research Focus
Key Achievements
Top Papers
- 1
- 2Information perception in modern poultry farming: A review115 citations · 2022
- 3Improved ResNet-50 deep learning algorithm for identifying chicken gender63 citations · 2023
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- 6DCDNet: A deep neural network for dead chicken detection in layer farms6 citations · 2025