# Emerging Technologies for Automation in Environmental Sensing: Review
## 3 Emerging Technologies in Environmental Sensing
### Harnessing the Power of Deep Learning in Environmental Sensing
Traditional farming causes deforestation in tropical rainforests for agriculture, and it leads to soil erosion, removing fertile topsoil that takes decades to replenish [69]. Deep learning, revolutionary across industries, transforms agriculture by integrating innovative solutions into traditional farming practices. Employing deep learning with drone technology is crucial for convenient crop monitoring through high-quality, high-resolution image capture [70]. This technology facilitates the identification of field advancements and quality assessment. For instance, using images from drone technology, agriculturalists can ascertain the readiness of crops for harvesting. The integration of this technology in agriculture has revolutionized farming. Researchers are particularly motivated to explore the applications of deep learning in enhancing efficiency, especially in farming, harvesting, and yield predictions. Precise fruit counting is vital for growers as it allows for yield estimation, facilitating effective yard management. Employing automated fruit detection and algorithms, as outlined in [71], becomes instrumental in optimizing agricultural production and streamlining the harvest process. In [72], the researchers proposed an automated yield estimation method using efficient robotic agricultural techniques. Achieving a high 91% accuracy with Inception-ResNet, the approach eliminates the need for an extensive dataset, benefiting farmers in efficient and precise fruit counting and decision-making. Undesirable plants, known as weeds, can hamper crop production by competing for resources. DL techniques, detailed in [73; 74], provide efficient weed identification, diminishing the need for weedicides and tackling herbicide resistance. Through the utilization of SVMs and CNNs, DL aids in classifying weeds, alleviating farmers' workload, and ultimately improving crop yields. Therefore, while deep learning has the potential to revolutionize agriculture, addressing robustness, interpretability, data modality integration, and implementing few-shot learning is crucial. Further research in these areas is necessary for unleashing the full power of deep learning in agriculture. Forecasting air pollution provides reliable data on upcoming pollution levels, assisting in the efficient implementation of air pollution control measures and enabling proactive planning. The intricacies of air pollution dynamics are commonly influenced by factors such as temperature, humidity, wind direction, wind speed, snowfall, rainfall, etc., intensifying  
Figure 4: Deep learning applications in environment sensing. the complexity of grasping changes in air pollutant concentration. Global concerns over air pollution stem from its adverse effects on human health, climate, agriculture, ecosystems, and visibility [75; 76; 77]. Particulate matter with an aerodynamic diameter of less than 2.5 m (PM2.5) and ozone (O3) are major contributors to premature mortality globally, especially in the case of PM2.5 pollution. Outdoor fine particulate and ozone air pollution are estimated to cause 8.34 million excess deaths annually, with O3 pollution accounting for nearly half a million deaths [78]. Table 3 summarizes various studies addressing air and water pollution monitoring and mitigation strategies. Each study is labeled with relevant information provided regarding the location of the study, the model or approach utilized, the performance measure used to evaluate effectiveness, and the key findings or outcomes.