Self Supervised Learning(SSL) has emerged as a prominent paradigm for label-efficient learning, and has been widely utilized by remote sensing foundation models(RSFMs). Recent RSFMs including SatMAE, DoFA, primarily rely on masked autoencoding(MAE), contrastive learning or some combination of them. However, these pretext tasks often overlook the unique temporal characteristics of agricultural landscape, namely nature's cycle. Motivated by this gap, we propose three novel agriculture-specific pretext tasks, namely Time-Difference Prediction(TD), Temporal Frequency Prediction(FP), and Future-Frame Prediction(FF). Comprehensive evaluation on SICKLE dataset shows FF achieves 69.6% IoU on crop mapping and FP reduces yield prediction error to 30.7% MAPE, outperforming all baselines, and TD remains competitive on most tasks. Further, we also scale FF to the national scale of India, achieving 54.2% IoU outperforming all baselines on field boundary delineation on FTW India dataset.
AGSPNet: A framework for parcel-scale crop fine-grained semantic change detection from UAV high-resolution imagery with agricultural geographic scene constraints
Hunan University of Science and Technology（湖南科技大学）
Early Classification for Agricultural Monitoring from Satellite Time Series
Technical University of Munich（慕尼黑工业大学）；Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA)（信息与随机系统研究所）；LETG-Rennes（雷恩地球与环境科学实验室）；Université de Bretagne Sud（南布列塔尼大学）
CBAM-STN-TPS-YOLO: Enhancing Agricultural Object Detection through Spatially Adaptive Attention Mechanisms
1 Department of Computer Science；Engineering, Texas A\&M University, College Station, TX, USA 2 Department of Statistics, Texas A\&M University, College Station, TX, USA satvikpraveen\；2 Department of Statistics, Texas A\&M University, College Station, TX, USA satvikpraveen\
The first global agricultural field boundary map at 10m resolution
Microsoft AI for Good Research Lab（微软AI for Good研究实验室）；Arizona State University（亚利桑那州立大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；Oregon State University（俄勒冈州立大学）；Clark University（克拉克大学）；Taylor Geospatial（泰勒地理空间）
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)
Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary Segmentation
Arizona State University（亚利桑那州立大学）；Microsoft AI for Good Research Lab（微软人工智能造福社会研究实验室）；Taylor Geospatial Institute（泰勒地理空间研究所）；Washington University in St Louis（圣路易斯华盛顿大学）；Taylor Geospatial Engine（泰勒地理空间引擎）
Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability
Leibniz-Institut für Agrartechnik und Bioökonomie e.V. (ATB)（莱布尼茨农业工程与生物经济研究所）；Technische Universität Berlin（柏林工业大学）；Understandable Machine Intelligence Lab (UMI Lab)（可理解机器智能实验室）；Universität Potsdam（波茨坦大学）
Recent applications of machine learning, remote sensing, and iot approaches in yield prediction: a critical review
International Water Research Institute (IWRI)（国际水研究所（IWRI））；Mohammed VI Polytechnic University (UM6P)（穆罕默德六世理工大学（UM6P））；Research and Development Unit in Agriculture and Agri-food of Abitibi-Témiscamingue (URDAAT)（阿比蒂比-泰米斯卡曼格农业与农业食品研发部（URDAAT））；Université du Québec en Abitibi-Témiscamingue（魁北克大学阿比蒂比-泰米斯卡曼格分校）
