A vision-language model for predicting potential distribution land of soybean double cropping

Introduction Accurately predicting suitable areas for double-cropped soybeans under changing climatic conditions is critical for ensuring food security anc optimizing land use. Traditional methods, relying on single-modal approaches such as remote sensing imagery or climate data in isolation, often fail to capture the complex interactions among environmental factors, leading to suboptimal predictions. Moreover, these approaches lack the ability to integrate multi-scale data and contextual information, limiting their applicability in diverse and dynamic environments. Methods To address these challenges, we propose AgriCLIP, anovel remote sensing vision-language model that integrates remote sensing imagery with textual data, such as climate reports and agricultural practices, to predict potential distribution areas of double-cropped soybeans under climate change. AgriCLIP employs advanced techniques including multi-scale data processing, self-supervised learning, and cross-modality feature fusion enabling comprehensive analysis of factors influencing crop suitability. Results and discussion Extensive evaluations on four diverse remote sensing datasets-RSICap RSIEval, MillionAID, and HRSID-demonstrate AgriCLIP’s superior performance over state-of-the-art models. Notably, AgriCLIP achieves a 97.54% accuracy or the RSICap dataset and outperforms competitors across metrics such as recall F1 score, and AUC. Its efficiency is further highlighted by reduced computation a demands compared to baseline methods. AgriCLIP’s ability to seamlessly integrate visual and contextual information not only advances prediction accuracy but also provides interpretable insights for agricultural planning and climate adaptation strategies, offering a robust and scalable solution for addressing the challenges of food security in the context of global climate change.