ArticlePlant phenomics (Washington, D.C.)2024
Toward Real Scenery: A Lightweight Tomato Growth Inspection Algorithm for Leaf Disease Detection and Fruit Counting.
Article in Plant phenomics (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 31 citations in OpenAlex.
- Plant counting: origins, development, and future paths.Plant phenomics (Washington, D.C.) · 2026Review
- Enhancing plant disease detection through multi-modal integration of visual and textual data.Plant methods · 2026Article
- Feature enhancement and fusion-optimized defect detection model for Sanhua plums.Frontiers in plant science · 2026Article
- Depth Imaging-Based Framework for Efficient Phenotypic Recognition in Tomato Fruit.Plants (Basel, Switzerland) · 2025Article
- Deep learning method for cucumber disease detection in complex environments for new agricultural productivity.BMC plant biology · 2025Article
- A Dynamic Kalman Filtering Method for Multi-Object Fruit Tracking and Counting in Complex Orchards.Sensors (Basel, Switzerland) · 2025Article
- An advanced deep learning method for pepper diseases and pests detection.Plant methods · 2025Article
- A Lightweight Framework for Protected Vegetable Disease Detection in Complex Scenes.Food science & nutrition · 2025Article
- TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection.Frontiers in plant science · 2025Article
- Intelligent deep learning architecture for precision vegetable disease detection advancing agricultural new quality productive forces.Frontiers in plant science · 2025Article
- Article
- Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition.Plant phenomics (Washington, D.C.) · 2024Article
- One to All: Toward a Unified Model for Counting Cereal Crop Heads Based on Few-Shot Learning.Plant phenomics (Washington, D.C.) · 2024Article
- TomatoGuard-YOLO: a novel efficient tomato disease detection method.Frontiers in plant science · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The deployment of intelligent surveillance systems to monitor tomato plant growth poses substantial challenges due to the dynamic nature of disease patterns and the complexity of environmental conditions such as background and lighting. In this study, an integrated cascade framework that synergizes detectors and trackers was introduced for the simultaneous identification of tomato leaf diseases and fruit counting. We applied an autonomous robot with smartphone camera to collect images for leaf disease and fruits in greenhouses. Further, we improved the deep learning network YOLO-TGI by incorporating Ghost and CBAM modules, which was trained and tested in conjunction with premier lightweight detection models like YOLOX and NanoDet in evaluating leaf health conditions. For the cascading with various base detectors, we integrated state-of-the-art trackers such as Byte-Track, Motpy, and FairMot to enable fruit counting in video streams. Experimental results indicated that the combination of YOLO-TGI and Byte-Track achieved the most robust performance. Particularly, YOLO-TGI-N emerged as the model with the least computational demands, registering the lowest FLOPs at 2.05 G and checkpoint weights at 3.7 M, while still maintaining a mAP of 0.72 for leaf disease detection. Regarding the fruit counting, the combination of YOLO-TGI-S and Byte-Track achieved the best
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.