{"total":54,"rows":[{"id":"2311.18803","title":"BioCLIP: A Vision Foundation Model for the Tree of Life","created":"2023-11-30","cited_by_count":161,"area":null},{"id":"2204.08790","title":"ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models","created":"2022-04-19","cited_by_count":141,"area":null},{"id":"2505.23883","title":"BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning","created":"2025-05-29","cited_by_count":50,"area":null},{"id":"2302.08909","title":"Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification","created":"2023-02-16","cited_by_count":30,"area":null},{"id":"2404.16233","title":"AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models","created":"2024-04-24","cited_by_count":30,"area":null},{"id":"2307.11035","title":"Cascade-DETR: Delving into High-Quality Universal Object Detection","created":"2023-07-20","cited_by_count":19,"area":null},{"id":"2408.03120","title":"Benchmarking In-the-wild Multimodal Disease Recognition and A Versatile Baseline","created":"2024-08-06","cited_by_count":19,"area":null},{"id":"2311.01619","title":"InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images","created":"2023-11-02","cited_by_count":15,"area":null},{"id":"2409.04038","title":"PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation","created":"2024-09-06","cited_by_count":14,"area":null},{"id":"2208.02707","title":"Standardizing and Centralizing Datasets to Enable Efficient Training of Agricultural Deep Learning Models","created":"2022-08-04","cited_by_count":7,"area":null},{"id":"2504.21706","title":"Vision Transformers in Precision Agriculture: A Comprehensive Survey","created":"2025-04-30","cited_by_count":6,"area":null},{"id":"2207.03881","title":"The Power of Transfer Learning in Agricultural Applications: AgriNet","created":"2022-07-08","cited_by_count":5,"area":null},{"id":"2302.10253","title":"Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness","created":"2023-02-20","cited_by_count":4,"area":null},{"id":"2410.12718","title":"RAFA-Net: Region Attention Network For Food Items And Agricultural Stress Recognition","created":"2024-10-16","cited_by_count":3,"area":null},{"id":"2503.20068","title":"iNatAg: Multi-Class Classification Models Enabled by a Large-Scale Benchmark Dataset with 4.7M Images of 2,959 Crop and Weed Species","created":"2025-03-25","cited_by_count":3,"area":null},{"id":"2510.20095","title":"BioCAP: Exploiting Synthetic Captions Beyond Labels in Biological Foundation Models","created":"2025-10-23","cited_by_count":3,"area":null},{"id":"2305.10084","title":"CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture","created":"2023-05-17","cited_by_count":2,"area":null},{"id":"2310.12509","title":"Machine Learning for Leaf Disease Classification: Data, Techniques and Applications","created":"2023-10-19","cited_by_count":2,"area":null},{"id":"2310.16273","title":"Deep Learning for Plant Identification and Disease Classification from Leaf Images: Multi-prediction Approaches","created":"2023-10-25","cited_by_count":2,"area":null},{"id":"2409.11338","title":"CLIP Adaptation by Intra-modal Overlap Reduction","created":"2024-09-17","cited_by_count":2,"area":null},{"id":"2410.12742","title":"PND-Net: Plant Nutrition Deficiency and Disease Classification using Graph Convolutional Network","created":"2024-10-16","cited_by_count":2,"area":null},{"id":"2507.04412","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","created":"2025-07-06","cited_by_count":2,"area":null},{"id":"2305.16484","title":"Batch Model Consolidation: A Multi-Task Model Consolidation Framework","created":"2023-05-25","cited_by_count":1,"area":null},{"id":"2504.01386","title":"DALIP: Distribution Alignment-based Language-Image Pre-Training for Domain-Specific Data","created":"2025-04-02","cited_by_count":1,"area":null},{"id":"2507.00070","title":"An efficient plant disease detection using transfer learning approach","created":"2025-06-28","cited_by_count":1,"area":null},{"id":"2508.10817","title":"Mobile-Friendly Deep Learning for Plant Disease Detection: A Lightweight CNN Benchmark Across 101 Classes of 33 Crops","created":"2025-08-14","cited_by_count":1,"area":null},{"id":"2605.08121","title":"Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification","created":"2026-04-28","cited_by_count":1,"area":null},{"id":"2608.08727","title":"TomaMMU: A Comprehensive Multimodal Understanding Benchmark for Tomato Leaf Diseases","created":"2026-08-09","cited_by_count":1,"area":null},{"id":"2308.14466","title":"Improving the performance of object detection by preserving label distribution","created":"2023-08-28","cited_by_count":0,"area":null},{"id":"2312.16331","title":"Early and Accurate Detection of Tomato Leaf Diseases Using TomFormer","created":"2023-12-26","cited_by_count":0,"area":null},{"id":"2401.06807","title":"An EcoSage Assistant: Towards Building A Multimodal Plant Care Dialogue Assistant","created":"2024-01-10","cited_by_count":0,"area":null},{"id":"2407.20279","title":"On Supernet Transfer Learning for Effective Task Adaptation","created":"2024-07-26","cited_by_count":0,"area":null},{"id":"2407.21652","title":"Spatial Transformer Network YOLO Model for Agricultural Object Detection","created":"2024-07-31","cited_by_count":0,"area":null},{"id":"2503.17526","title":"Beyond the Encoder: Joint Encoder-Decoder Contrastive Pre-Training Improves Dense Prediction","created":"2025-03-21","cited_by_count":0,"area":null},{"id":"2506.05972","title":"Domain Adaptation for Big Data in Agricultural Image Analysis: A Comprehensive Review","created":"2025-06-06","cited_by_count":0,"area":null},{"id":"2506.14842","title":"PictSure: Pretraining Embeddings Matters for In-Context Learning Image Classifiers","created":"2025-06-16","cited_by_count":0,"area":null},{"id":"2507.20125","title":"Multi-output Deep-Supervised Classifier Chains for Plant Pathology","created":"2025-07-27","cited_by_count":0,"area":null},{"id":"2512.09492","title":"StateSpace-SSL: Linear-Time Self-supervised Learning for Plant Disease Detection","created":"2025-12-10","cited_by_count":0,"area":null},{"id":"2601.02353","title":"Meta-Learning Guided Pruning for Few-Shot Plant Pathology on Edge Devices","created":"2026-01-05","cited_by_count":0,"area":null},{"id":"2601.07970","title":"Sesame Plant Segmentation Dataset: A YOLO Formatted Annotated Dataset","created":"2026-01-12","cited_by_count":0,"area":null},{"id":"2601.11612","title":"Domain-Specific Self-Supervised Pre-training for Agricultural Disease Classification: A Hierarchical Vision Transformer Study","created":"2026-01-09","cited_by_count":0,"area":null},{"id":"2603.13354","title":"AgriPath: A Systematic Exploration of Architectural Trade-offs for Crop Disease Classification","created":"2026-03-08","cited_by_count":0,"area":null},{"id":"2603.15085","title":"Affordable Precision Agriculture: A Deployment-Oriented Review of Low-Cost, Low-Power Edge AI and TinyML for Resource-Constrained Farming Systems","created":"2026-03-16","cited_by_count":0,"area":null},{"id":"2606.02045","title":"Quantifying and Mitigating Domain Shift in Peach Leaf Damage Classification: Attention Mechanisms and Fine-Tuning Strategies","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2608.07984","title":"AgriField-40K: Adapting Vision Models to Agriculture With Efficient Continual Pretraining","created":"2026-08-08","cited_by_count":0,"area":null},{"id":"2608.14235","title":"AppleScab-LT: A Longitudinal Real-Field Apple Scab Dataset for Temporal Disease Progression Analysis","created":"2026-08-14","cited_by_count":0,"area":null},{"id":"2608.25539","title":"CropCop: An Auditable 120-Class Plant-Health Model from Benchmark Reconstruction to a Quantised Runtime Artifact","created":"2026-08-26","cited_by_count":0,"area":null},{"id":"2609.14654","title":"Evaluating Contextual Bias in CNN Image Classification: Evidence from Agricultural Benchmark Datasets","created":"2026-09-13","cited_by_count":0,"area":null},{"id":"2609.20290","title":"TinyCNN: A 193K-Parameter Network for On-Device Plant Disease Detection, with a Cross-Dataset Robustness Diagnosis","created":"2026-07-29","cited_by_count":0,"area":null},{"id":"2609.20325","title":"AgriScope: Pixel-Grounded Multimodal Understanding for Agricultural Images","created":"2026-09-17","cited_by_count":0,"area":null},{"id":"2609.21651","title":"Configurable Multi-Stage Vision Pipeline for Crop Disease and Pest Diagnosis","created":"2026-09-18","cited_by_count":0,"area":null},{"id":"2610.04425","title":"AgroGround: Multi-Granularity Grounded Recognition in Agriculture","created":"2026-10-03","cited_by_count":0,"area":null},{"id":"2610.04456","title":"Multi-Crop Leaf Disease Recognition: A Unified Benchmark and Cross-Region Study","created":"2026-10-03","cited_by_count":0,"area":null},{"id":"2610.15049","title":"Use Of Convolutional Neural Networks In Detection Of Branch Dieback Disease In Pequi Trees","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}