# Emerging Technologies for Automation in Environmental Sensing: Review
## 4 Challenges and Solutions in Implementing Automation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technology** & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Limitations**} \\ \hline \multirow{7}{*}{Transfer Learning} & - & Efficient data collection: enhances machine learning (ML) data collection by leveraging knowledge from previous tasks, reducing the need for extensive target domain data. & - & Negative transfer risk: the approach may lead to negative transfer, diminishing overall performance. \\  & - & Positive transfer: avoids negative transfer, improving overall performance. & - & Complexity: heterogeneous transfer learning is more intricate, requiring feature space adaptation. \\  & - & Categorization: homogeneous and heterogeneous transfer learning strategies cater to situations with identical or different attributes. & - & Classification challenges: inductive, transductive, and unsupervised learning face variations in source and target datasets. \\ \hline \multirow{7}{*}{Deep Learning} & - & Broad application domains: applicable across various domains, including object detection, predictive maintenance, agriculture, robotic surgery, and autonomous driving. & - & Robustness challenges: needs to address challenges related to robustness, interpretability, and data modality integration. \\  & - & Automation capability: contributes to transformative advancements in agriculture and other industries. & - & Data efficiency: achieving high accuracy may require extensive datasets, impacting efficiency. \\  & - & Precision in agriculture: facilitates precise fruit counting, optimizing production and harvest processes. & - & Privacy concerns: applications may raise privacy concerns, especially in smart agriculture and autonomous systems. \\ \hline \multirow{7}{*}{Edge Computing} & - & Real-time analysis: enables real-time intelligent analysis by processing data in proximity to physical surroundings, reducing latency and bandwidth utilization. & - & Privacy and security: poses challenges related to privacy and security, especially with amplified data transmission from edge devices. \\  & - & Energy efficiency: minimizes energy consumption in data centers, boosting energy efficiency. & - & Energy consumption: growing smart device numbers can lead to substantial energy consumption in data centers, impacting efficiency. \\  & - & Localized services: focuses on local context, effective for small-scale, real-time intelligent analysis and localized services. & - & Load management: may face challenges in load management, real-time performance, and bandwidth during extensive data processing. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Advantages and disadvantages of technologies in environmental sensing. researchers, practitioners, policymakers, and industry players can find innovative solutions and overcome obstacles to automation in environmental sensing and monitoring. Automation technologies, along with advanced methods like machine learning (ML), deep learning (DL), transfer learning, and edge computing, offer promising solutions to enhance environmental monitoring effectively. ML algorithms can analyze extensive environmental data, identify patterns, and anomalies, and predict changes, which helps create early warning systems for natural disasters or pollution events [106]. DL techniques, especially Convolutional Neural Networks (CNNs), excel at recognizing images, making them valuable for tasks like monitoring wildlife habitats or identifying pollution sources from satellite imagery or drone footage accurately. Transfer learning allows adapting pre-trained models to specific environmental monitoring tasks by transferring knowledge from one domain to another, speeding up model training and enhancing performance, especially when labeled environmental data are limited. Edge computing, bringing computational capabilities closer to data sources, enables real-time processing and analysis, reducing latency and bandwidth requirements, making it ideal for air quality monitoring or wildlife tracking in remote areas.