# Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems
## 2 Related Work and Research Gaps
The techniques developed in this study can be suited for urban agriculture, where space is limited, and plants may be grown in non-traditional settings such as rooftops, balconies, and indoor gardens. The non-contact and non-invasive nature of thermal imaging aligns with the need for minimal disturbance in such environments. Furthermore, the model's reliance on easily obtainable input factors like leaf temperature and solar radiation makes it accessible for urban farmers who might not have extensive technical resources. Traditional methods for estimating transpiration rates often face challenges in urban environments due to the difficulty in obtaining or calculating parameters like aerodynamic resistance and canopy resistance. The 3D-3T model's advantage lies in its independence from these complex parameters, making it a straightforward and potentially more accurate method for urban plant monitoring. In plant monitoring and agricultural technology, there has been considerable progress in developing specialized methods for specific aspects of plant health assessment. However, a notable gap in the current research is the lack of integration between different domains, particularly in the fusion of computer vision techniques with sensor data monitoring for comprehensive plant health forecasting. The studies that were reviewed were advanced in their respective areas, but they are isolated from one another. For instance, research that was utilizing infrared thermal imaging and the 3D-3T model focuses primarily on estimating canopy transpiration rates in plants [12]. This approach, though innovative in its use of thermal imaging, does not incorporate other vital sensor data that could offer a more holistic view of plant health. Similarly, the study on black gram plant nutrient deficiency classification using convolutional neural networks (CNNs) in [11] demonstrates the potential of computer vision in identifying specific nutrient deficiencies through image analysis. However, this research does not extend to the integration of sensor data which could provide additional context such as soil moisture levels, nutrient concentrations, or environmental conditions that are crucial for a comprehensive understanding of plant health. Another study, related to the utilization of CNN based regression models, has proven to be an effective approach for estimating correlated color temperature (CCT) from RGB images [13]. CNN, which are variants of multilayer perceptrons, mimic the functionality of the human visual cortex and are well suited for image processing tasks. The adaptability of CNNs have been proven in study [13] to handle large scale image datasets, delivering results that confirm their capability in processing not only static two-dimensional images but also dynamic three-dimensional video data. This attribute of CNNs, when used in a plant monitoring domain for thermal imagery use cases, indicates a promising approach for accurately forecasting plant health indicators by interpreting the thermal patterns that are indicative of a plant physiological state. The research presented in [14] describes the integration of infrared thermography (IRT) in the assessment of soil moisture content, demonstrating a non-destructive and non-contact method that holds promise for advanced plant monitoring systems. Through the application of an external halogen lamp light and capturing soil surface temperatures with an infrared camera, the study achieved a high correlation between thermal data and soil moisture content. This technique's successful estimation of moisture content highlightsthe potential of IRT to serve as a valuable tool in precision agriculture, providing a means to monitor soil conditions closely without physically altering the soil environment. Furthermore, the relationship between the soil's surface temperature and its moisture content was established with a high degree of accuracy, showcasing a clear inverse correlation--as the moisture content decreased, the temperature rose, and vice versa.