# Emerging Technologies for Automation in Environmental Sensing: Review
## 3 Emerging Technologies in Environmental Sensing
### Harnessing the Power of Deep Learning in Environmental Sensing  
The Fourth Industrial Revolution generally concentrated on technology-driven \"industrialization, smart and intelligent systems\", where deep learning (DL) [60] has evolved as one of the vital technologies. It is a frontier for AI, based on linear regression and artificial neural networks (ANNs) followed by some activation function, which trains the systems to process data in a manner motivated by the human brain. DL is widely applied in various application areas like automated driving to medical devices, automatic facial recognition systems, digital assistants and fraud detection, healthcare, cybersecurity, and many more [61]. Hence, its automation capability and understanding from prior knowledge can transform the globe and our daily lives. Figure 3 depicts the deep learning workflow, which includes data preparation, model design, training, validation, testing, and deployment stages. It begins with data collection and preprocessing, followed by the design of a neural network architecture tailored to the task. The model is then trained using labeled data to minimize error, validated for generalization to unseen examples, and tested on new data. Upon successful validation, the trained model is deployed for real-world applications. DL has emerged as a universal learning due to its capability to perform in approximately all application domains. Figure 4 represents the various DL applications in environment sensing. The DL applications are found in object detection and recognition [62], predictive maintenance [63], agriculture [64], robotic surgery [65], medical applications [66], autonomous driving [67], etc. The robots can recognize and categorize things in their surroundings with increased precision, by training neural networks with huge quantities of labeled data. Also, by analyzing sensor data, predictive maintenance algorithms can foresee when a robot's segments may fail, permitting proactive repairs or replacements. DL has the prospect of revolutionizing the farming industry by allowing more efficient crop production by autonomously guiding and managing crops and lowering labor expenses in agribusiness, such as planting, harvesting, spraying, etc. [68]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & **Dataset Name** & **Location** & **Purpose** & **Number of** & **Number** & **Bands** & **Resolution** & **Pixel Count** \\ \hline \multirow{3}{*}{[55]} & DeepWeeds & Australia &
\begin{tabular}{c} Weed detection \\ and classification \\ \end{tabular} & 17,509 & 8 & RGB & NA & 40.3 B \\  & The Crop/Weed & \multirow{2}{*}{Global} & Crop and weed & \multirow{2}{*}{60} & \multirow{2}{*}{2} & \multirow{2}{*}{RGB} & \multirow{2}{*}{NA} & \multirow{2}{*}{0.08 B} \\  & Field Image & & discrimination & & & & & \\  & Dataset & & Land use & & & & & \\  & Aerial & \multirow{2}{*}{Global} & classification & \multirow{2}{*}{10,000} & \multirow{2}{*}{30} & \multirow{2}{*}{RGB} & \multirow{2}{*}{8 M/0.5 M} & \multirow{2}{*}{NA} \\  & & & from aerial & images & & & & \\  & & & Field anomaly & & & & & \\  & & Dataset & USA & pattern & 94,986 & 9 & Near-Infrared & 10/15/20/Cm/Px & 22.6 B \\  & & & segmentation & & & (NIR) & \\  & & Land use & & & & & & \\  & & & classification & & & & & \\  & & USA & from satellite & \multirow{2}{*}{856,000} & \multirow{2}{*}{137} & \multirow{2}{*}{Near-Infrared (NIR)} & \multirow{2}{*}{10 m/1 m} & \multirow{2}{*}{5 M} \\  & & & imagery & & & & (NIR) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Commonly used datasets for transfer learning in remote sensing. Figure 3: Deep learning workflow [60]. Traditional farming incorporates agroforestry, crop rotation, intercropping, polycultures, and water harvesting practices. Before scientific advancements, traditional agriculture relied on tools, organic fertilizers, indigenous knowledge, and cultural beliefs. Traditional farming depletes soil nutrients, with practices like slash and burn reducing soil organic matter.