The field of plant disease detection and stress analysis is witnessing significant advancements with the integration of innovative machine learning and computer vision techniques. Researchers are focusing on developing efficient and robust methods for detecting plant diseases and stress, leveraging advancements in deep learning, self-supervised learning, and contrastive learning. These approaches aim to address the limitations of traditional methods, such as the need for extensive labeled datasets and high computational costs. The use of lightweight vision transformers, bidirectional state space models, and eigenvector-guided contrastive learning are some of the key innovations in this area. Noteworthy papers include MangoLeafViT, which proposes a lightweight Vision Transformer-based pipeline for mango leaf disease classification, and ConMamba, which introduces a novel self-supervised learning framework for plant disease detection. Temporal Vegetation Index-Based Unsupervised Crop Stress Detection via Eigenvector-Guided Contrastive Learning is also a notable work, presenting a label-free approach for early stress detection.