Description
Book SynopsisWild Berry image dataset collected in Finnish forests and peatlands using drones.- Soybean pod and seed counting in both outdoor fields and indoor laboratories using unions of deep neural networks.- A Framework for Enhanced Decision Support in Digital Agriculture Using Explainable Machine Learning.- Lincoln's Annotated Spatio-Temporal Strawberry Dataset (LAST-Straw).- 3D Phenotyping of Canopy Occupation Volume as a Major Predictor for Canopy Photosynthesis in Rice (Oryza sativa L.).- Retrieval of sun-induced plant fluorescence in the O2-A absorption band from DESIS imagery.- Unsupervised Tomato Split Anomaly Detection using Hyperspectral Imaging and Variational Autoencoders.- KAN You See It? KANs and Sentinel for Effective and Explainable Crop Field Segmentation.- RoWeeder: Unsupervised Weed Mapping through Crop-Row Detection.- Consolidation of symbolic instances using sensor data via tracklet merging for long-term monitoring of crops.- Automated Generation of Accurate, Compact and Focused Crop and Weed Segmentation Models.- Comparative Analysis of YOLOv9, YOLOv10 and RT-DETR for Real-Time Weed Detection.- Towards Auto-Generated Ground Truth for Evaluation of Perception Systems in Agriculture.- AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models.- Deep Learning Based Growth Modeling of Plant Phenotypes.- A simple approach to pavement cell segmentation.- Enhancing weed detection performance by means of GenAI-based image augmentation.- SynthSet: Generative Diffusion Model for Semantic Segmentation in Precision Agriculture.- Robust UDA for Crop and Weed Segmentation: Multi-Scale Attention and Style-Adaptive Techniques.- Ordinal-Meta Learning for Fine-grained Fruit Quality Prediction.- Beyond Annotations: Efficient Wheat Head Segmentation Using L-Systems, Game Engines, and Student-Teacher Models.- Exploiting Boundary Loss for the Hierarchical Panoptic Segmentation of Plants and Leaves.
