Papers Crop Yield Prediction
“Crop Yield Prediction” 태그가 달린 논문 61편 · 필터 해제
PhenoYieldNet: Learning Crop-Aware Phenological Responses for Multi-Crop Yield Prediction
Accurate crop yield prediction is crucial for sustainable agriculture and global food security. While existing methods are predominantly developed for single-crop prediction, they often struggle to generalize across dive…
Crop Yield PredictionAttention-based Multi-modal Deep Learning Model of Spatio-temporal Crop Yield Prediction with Satellite, Soil and Climate Data
Crop yield prediction is one of the most important challenge, which is crucial to world food security and policy-making decisions. The conventional forecasting techniques are limited in their accuracy with reference to t…
Crop Yield PredictionYieldSAT: A Multimodal Benchmark Dataset for High-Resolution Crop Yield Prediction
Crop yield prediction requires substantial data to train scalable models. However, creating yield prediction datasets is constrained by high acquisition costs, heterogeneous data quality, and data privacy regulations. Co…
Crop Yield PredictionRetrieval-Augmented Multi-scale Framework for County-Level Crop Yield Prediction Across Large Regions
This paper proposes a new method for crop yield prediction, which is essential for developing management strategies, informing insurance assessments, and ensuring long-term food security. Although existing data-driven ap…
Crop Yield PredictionUniCrop: A Universal, Multi-Source Data Engineering Pipeline for Scalable Crop Yield Prediction
Accurate crop yield prediction relies on diverse data streams, including satellite, meteorological, soil, and topographic information. However, despite rapid advances in machine learning, existing approaches remain crop-…
Crop Yield PredictionHarvesting AlphaEarth: Benchmarking the Geospatial Foundation Model for Agricultural Downstream Tasks
Geospatial foundation models (GFMs) have emerged as a promising approach to overcoming the limitations in existing featurization methods. More recently, Google DeepMind has introduced AlphaEarth Foundation (AEF), a GFM p…
Crop Yield PredictionFARM: Fine-Tuning Geospatial Foundation Models for Intra-Field Crop Yield Regression
Accurate and timely crop yield prediction is crucial for global food security and modern agricultural management. Traditional methods often lack the scalability and granularity required for precision farming. This paper …
Crop Yield PredictionInformed Learning for Estimating Drought Stress at Fine-Scale Resolution Enables Accurate Yield Prediction
Water is essential for agricultural productivity. Assessing water shortages and reduced yield potential is a critical factor in decision-making for ensuring agricultural productivity and food security. Crop simulation mo…
Crop Yield PredictionHierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systems
In this paper, we presents a novel hierarchical federated learning architecture specifically designed for smart agricultural production systems and crop yield prediction. Our approach introduces a seasonal subscription m…
Crop Yield PredictionFederated LearningTasselNetV4: A vision foundation model for cross-scene, cross-scale, and cross-species plant counting
Accurate plant counting provides valuable information for agriculture such as crop yield prediction, plant density assessment, and phenotype quantification. Vision-based approaches are currently the mainstream solution. …
Crop Yield PredictionA multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield prediction
Precise yield prediction is essential for agricultural sustainability and food security. However, climate change complicates accurate yield prediction by affecting major factors such as weather conditions, soil fertility…
Crop Yield PredictionContrastive LearningIntrinsic Explainability of Multimodal Learning for Crop Yield Prediction
Multimodal learning enables various machine learning tasks to benefit from diverse data sources, effectively mimicking the interplay of different factors in real-world applications, particularly in agriculture. While the…
Crop Yield PredictionDFYP: A Dynamic Fusion Framework with Spectral Channel Attention and Adaptive Operator learning for Crop Yield Prediction
Accurate remote sensing-based crop yield prediction remains a fundamental challenging task due to complex spatial patterns, heterogeneous spectral characteristics, and dynamic agricultural conditions. Existing methods of…
Crop Yield PredictionMT-CYP-Net: Multi-Task Network for Pixel-Level Crop Yield Prediction Under Very Few Samples
Accurate and fine-grained crop yield prediction plays a crucial role in advancing global agriculture. However, the accuracy of pixel-level yield estimation based on satellite remote sensing data has been constrained by t…
Crop ClassificationCrop Yield PredictionPredictionA Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation viaSynergistic Pseudo-Labeling and Generative Learning
Remote sensing enables a wide range of critical applications such as land cover and land use mapping, crop yield prediction, and environmental monitoring. Advances in satellite technology have expanded remote sensing dat…
Crop Yield PredictionDomain AdaptationDomain GeneralizationSemantic SegmentationMulti-modal Data Fusion and Deep Ensemble Learning for Accurate Crop Yield Prediction
This study introduces RicEns-Net, a novel Deep Ensemble model designed to predict crop yields by integrating diverse data sources through multimodal data fusion techniques. The research focuses specifically on the use of…
Crop Yield PredictionEnsemble LearningIntegrating remote sensing data assimilation, deep learning and large language model for interactive wheat breeding yield prediction
Yield is one of the core goals of crop breeding. By predicting the potential yield of different breeding materials, breeders can screen these materials at various growth stages to select the best performing. Based on unm…
Crop Yield PredictionLanguage ModelingLanguage ModellingLarge Language Model+1CMAViT: Integrating Climate, Managment, and Remote Sensing Data for Crop Yield Estimation with Multimodel Vision Transformers
Crop yield prediction is essential for agricultural planning but remains challenging due to the complex interactions between weather, climate, and management practices. To address these challenges, we introduce a deep le…
Crop Yield PredictionManagementFederated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Application
Federated learning has become an emerging technology for data analysis for IoT applications. This paper implements centralized and decentralized federated learning frameworks for crop yield prediction based on Long Short…
Crop Yield PredictionFederated LearningPredictionExplainability of Sub-Field Level Crop Yield Prediction using Remote Sensing
Crop yield forecasting plays a significant role in addressing growing concerns about food security and guiding decision-making for policymakers and farmers. When deep learning is employed, understanding the learning and …
Crop Yield PredictionDecision MakingFeature Importance