9 citations · 10 across the 6 of their papers we have counts for
7 papers
Benchmarking Vision-Language Models for Microscopic Plant Image Understanding
Tianqi Wei, Xin Yu, Zhi Chen +2
Microscopic imaging provides essential visual evidence for studying plant biology and pathology at the cellular and subcellular levels. However, existing benchmarks on vision-langu…
Augment to Segment: Tackling Pixel-Level Imbalance in Wheat Disease and Pest Segmentation
Tianqi Wei, Xin Yu, Zhi Chen +2
Accurate segmentation of foliar diseases and insect damage in wheat is crucial for effective crop management and disease control. However, the insect damage typically occupies only…
Track Any Peppers: Weakly Supervised Sweet Pepper Tracking Using VLMs
Jia Syuen Lim, Yadan Luo, Zhi Chen +3
In the Detection and Multi-Object Tracking of Sweet Peppers Challenge, we present Track Any Peppers (TAP) - a weakly supervised ensemble technique for sweet peppers tracking. TAP l…
CF-PRNet: Coarse-to-Fine Prototype Refining Network for Point Cloud Completion and Reconstruction
Zhi Chen, Tianqi Wei, Zecheng Zhao +6
In modern agriculture, precise monitoring of plants and fruits is crucial for tasks such as high-throughput phenotyping and automated harvesting. This paper addresses the challenge…
PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation
Tianqi Wei, Zhi Chen, Xin Yu +3
Plant diseases pose significant threats to agriculture. It necessitates proper diagnosis and effective treatment to safeguard crop yields. To automate the diagnosis process, image…
Snap and Diagnose: An Advanced Multimodal Retrieval System for Identifying Plant Diseases in the Wild
Tianqi Wei, Zhi Chen, Xin Yu
Plant disease recognition is a critical task that ensures crop health and mitigates the damage caused by diseases. A handy tool that enables farmers to receive a diagnosis based on…
Benchmarking In-the-wild Multimodal Disease Recognition and A Versatile Baseline
Tianqi Wei, Zhi Chen, Zi Huang +1
Existing plant disease classification models have achieved remarkable performance in recognizing in-laboratory diseased images. However, their performance often significantly degra…