# Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems
## 2 Related Work and Research Gaps
This finding is pivotal for plant monitoring, as it underscores the feasibility of using thermal data to infer critical environmental parameters that affect plant health, such as soil moisture. The ability to derive such parameters from thermal images could enable the development of advanced machine learning models, including CNNs, to forecast different types of plant monitoring sensor measurements. Similarly, the study [15] provides advancements for urban agriculture by employing thermal imaging to gauge water stress in citrus plants, a technique that could be used for plant monitoring in densely populated areas where plant growing spaces are limited. By directly examining plant canopy temperatures in a controlled greenhouse environment, researchers were able to detect the water status of one-year-old citrus plants, detecting water stress through variations in canopy temperature [15]. This noninvasive method showcased a strong correlation between irrigation levels and plant temperature responses, with thermal cameras efficiently showing plants under a water deficit by identifying higher canopy temperatures compared to ambient air. The research underpins the utility of thermal data as a reliable metric for plant monitoring, suggesting that such techniques, when integrated with automated systems, could significantly enhance irrigation scheduling in urban agricultural practices [15]. This approach has the potential to optimize water use and ensure plant health in urban environments, where efficient resource management is crucial. Regarding the deep neural network architecture in this context, papers [16; 17] demonstrate how CNN architectures can be used in extracting complex features from images for forecasting. The study [16] illustrates how CNNs can estimate tropical cyclone intensity from satellite imagery by interpreting visual data for weather analysis. The paper [17] extends this capability to \"El Nino\" forecasting, by analyzing spatial patterns in climate data. These studies describe the potential of CNNs in models designed for regression tasks, where single output values are forecasted from image data, by correlating visual patterns with specific sensor measurement values. Unfortunately, the absence of a unified approach that combines the strengths of both computer vision and sensor data monitoring and analysis is a significant limitation in the current research in this area. Current methodologies focus on singular aspects of plant health, such as nutrient deficiencies or transpiration rates, without exploring the potential connections that could arise from a combined analysis of visual data and sensor readings. This approach results in a fragmented understanding of plant health, where each method provides insights into specific aspects without offering a comprehensive overview. A more integrated approach, where computer vision applications are used in combination with sensor data monitoring, could revolutionize plant health forecasting. By combining the detailed visual analysis possible through computer vision techniques with the data provided by sensors, including thermal imagery, researchers could develop models capable of forecasting multiple sensor reading values from related sensors and parameters. This would not only enhance the accuracy of plant health assessments but also enable the forecasting of various plant health outcomes, leading to a more effective plant growth process.