The field of agricultural monitoring and inspection is experiencing significant growth, driven by the increasing adoption of artificial intelligence (AI) and deep learning techniques. Recent developments have focused on improving the accuracy and efficiency of plant disease and pest detection, as well as enhancing the monitoring of crop health and quality. Notably, researchers have been exploring the use of lightweight convolutional neural networks (CNNs) and vision transformers for real-time quality inspection and species identification. These advancements have the potential to revolutionize the agricultural industry by enabling early detection of diseases, reducing crop losses, and promoting sustainable farming practices. Noteworthy papers in this area include: DragonFruitQualityNet, which presents a lightweight CNN for real-time dragon fruit quality inspection on mobile devices, achieving an impressive 93.98% accuracy. From Field to Drone, which introduces an improved segmentation model for automated multi-species and damage plant semantic segmentation, demonstrating robust performance under domain shift. 3D Plant Root Skeleton Detection and Extraction, which proposes a method for efficiently deriving the 3D architecture of plant roots from a few images, showing considerable similarity to the ground truth. Evaluation of State-of-the-Art Deep Learning Techniques for Plant Disease and Pest Detection, which reviews modern computer-based techniques for detecting plant diseases and pests, highlighting the consistent superiority of modern AI-based approaches. Autonomous AI Bird Feeder for Backyard Biodiversity Monitoring, which presents a low-cost, on-premise system for autonomous backyard bird monitoring, achieving high validation performance and practical field accuracy. Multi-Label Plant Species Prediction with Metadata-Enhanced Multi-Head Vision Transformers, which presents a multi-head vision transformer approach for multi-label plant species prediction, demonstrating strong performance on the PlantCLEF 2025 challenge. Mobile-Friendly Deep Learning for Plant Disease Detection, which develops a mobile-friendly solution for accurately classifying 101 plant diseases across 33 crops, achieving a promising 94.7% classification accuracy with EfficientNet-B1.