The field of environmental monitoring and precision agriculture is rapidly advancing with the development of innovative technologies and methods. Recent research has focused on improving the accuracy and efficiency of monitoring systems, enabling early detection of diseases and pests, and promoting sustainable agricultural practices. The use of machine learning, deep learning, and computer vision techniques has been particularly noteworthy, allowing for the analysis of complex data sets and the development of predictive models. These advancements have the potential to significantly impact crop yields, reduce environmental degradation, and improve food security. Notable papers in this area include the development of a deep-learning-based model for predicting water quality, a robotic multimodal data acquisition system for estimating cover crop biomass, and a method for comprehensively evaluating pest counting confidence in images. Additionally, research on plant disease detection using transfer learning approaches and the development of lightweight shrimp disease detection models have shown promising results. Overall, these advancements demonstrate the potential for technology to drive positive change in environmental monitoring and precision agriculture.