Agriculture is the foundational industry for human survival, profoundly impacting economic, ecological, and social dimensions. In the face of global challenges such as rapid population growth, resource scarcity, and climate change, achieving technological innovation in agriculture and advancing smart farming have become increasingly critical. In recent years, deep learning and computer vision have developed rapidly. Key areas in computer vision—such as deep learning-based image processing, object detection, and multimodal fusion—are rapidly transforming traditional agricultural practices. Processes in agriculture, including planting planning, growth management, harvesting, and post-harvest handling, are shifting from experience-driven methods to digital and intelligent approaches. This paper systematically reviews applications of deep learning and computer vision in agricultural growth management over the past decade, categorizing them into four key areas: crop identification, grading and classification, disease monitoring, and weed detection. Additionally, we introduce classic methods and models in computer vision and deep learning, discussing approaches that utilize different types of visual information. Finally, we summarize current challenges and limitations of existing methods, providing insights for future research and promoting technological innovation in agriculture.

Category-level 6-D object pose tracking is very challenging in the field of 3-D computer vision. Keypoint-based object pose estimation has demonstrated its effectiveness in dealing with it. However, current approaches first estimate the keypoints through a neural network and further compute the interframe pose change via least-squares optimization. They estimate rotation and translation in the same way, ignoring the differences between them. In this work, we propose a keypoint-based disentangled pose network, which disentangles the 6-D object pose change to 3-D rotation and 3-D translation. Specifically, the translation is directly estimated by the network and the rotation is indirectly calculated by singular value decomposition according to the keypoints. Extensive experiments on the NOCS-REAL275 dataset demonstrate the superiority of our method.

Depth map plays an important role in our daily life including automatic driving, intelligent robots, and commercial manufacture. However, existing depth estimation methods are still facing some difficulties in real-life applications. Firstly, using merely point-wise metrics during training usually causes surface distortion due to the lack of geometric restrictions. To tackle this problem, we introduce a normal restriction module to the network to improve the method performance with regards to surface integrity. Secondly, although 3D methods usually achieve higher accuracy by directly concatenating features as cost aggregation, they sacrifice the time and memory efficiency. To make the disparity estimation network light enough for adding the normal restriction, we propose a 2D aggregation method to replace the 3D ones. Experiments prove the effectiveness of our method and we show that our results are competitive to 3D ethods under popular benchmarks including KITTI, SceneFlow and Midddlebury.

A high precision method for position and attitude determination is presented. One of the difficulties in pose determination is the lack of reliable visual features. As a result, we exploit a novel convolutional neural network for stereo matching, so as to solve the problem of image feature extraction. The network is based on a siamese network and incorporates dense block into each branch. There is occlusion problem in the close-range phase of measuring object. In order to solve the occlusion, a multi-view system is established. The measurement results meet the accuracy requirements of pose determination and verify the feasibility of the method.