Advances in Environmental Monitoring and Precision Agriculture

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.

Sources

Transfer Learning for Assessing Heavy Metal Pollution in Seaports Sediments

Robotic Multimodal Data Acquisition for In-Field Deep Learning Estimation of Cover Crop Biomass

Counting with Confidence: Accurate Pest Monitoring in Water Traps

When Plants Respond: Electrophysiology and Machine Learning for Green Monitoring Systems

An efficient plant disease detection using transfer learning approach

Machine Learning-based Early Detection of Potato Sprouting Using Electrophysiological Signals

Advancements in Weed Mapping: A Systematic Review

Crop Pest Classification Using Deep Learning Techniques: A Review

Advanced Printed Sensors for Environmental Applications: A Path Towards Sustainable Monitoring Solutions

Neural Network-based Study for Rice Leaf Disease Recognition and Classification: A Comparative Analysis Between Feature-based Model and Direct Imaging Model

Lightweight Shrimp Disease Detection Research Based on YOLOv8n

Detecting Multiple Diseases in Multiple Crops Using Deep Learning

IMASHRIMP: Automatic White Shrimp (Penaeus vannamei) Biometrical Analysis from Laboratory Images Using Computer Vision and Deep Learning

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