The field of remote sensing and plant disease diagnosis is witnessing significant advancements with the integration of deep learning techniques, particularly vision transformers and multimodal learning approaches. Researchers are addressing the limitations of existing models, such as their inability to generalize to real-world field images and the need for expensive data collection methods. Innovative solutions, including zero-shot learning, adaptive tuning engines, and training-free ultra-high-resolution remote sensing visual question answering, are being explored to improve the accuracy, efficiency, and interpretability of these models. The development of domain-aware adaptation frameworks, co-training vision language models, and ensemble-of-specialist models are also noteworthy. These breakthroughs have the potential to revolutionize the field, enabling more accurate and efficient plant disease diagnosis, species detection, and remote sensing applications. Notable papers include: Rethinking Plant Disease Diagnosis: Bridging the Academic-Practical Gap with Vision Transformers and Zero-Shot Learning, which demonstrates the potential of zero-shot learning for plant disease classification. What You See is (Usually) What You Get: Multimodal Prototype Networks that Abstain from Expensive Modalities, which introduces a cost-aware multimodal approach for species detection. CrossEarth-Gate: Fisher-Guided Adaptive Tuning Engine for Efficient Adaptation of Cross-Domain Remote Sensing Semantic Segmentation, which proposes a novel adaptive tuning engine for remote sensing semantic segmentation. Look Where It Matters: Training-Free Ultra-HR Remote Sensing VQA via Adaptive Zoom Search, which presents a training-free approach for ultra-high-resolution remote sensing visual question answering. BotaCLIP: Contrastive Learning for Botany-Aware Representation of Earth Observation Data, which introduces a lightweight multimodal contrastive framework for adapting foundation models to domain-specific tasks. Co-Training Vision Language Models for Remote Sensing Multi-task Learning, which presents a simple yet flexible vision language model baseline for remote sensing multi-task learning. EoS-FM: Can an Ensemble of Specialist Models act as a Generalist Feature Extractor?, which proposes a novel ensemble-of-specialist framework for building remote sensing foundation models.