# Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems
## 2 Related Work and Research Gaps  
Machine learning and computer vision technologies have demonstrated immense potential in transforming agricultural practices, especially in plant monitoring [6]. These technologies offer advanced capabilities for early detection and prediction of various plant health conditions, which are crucial for sustainable and efficient agricultural practices. For instance, deep learning algorithms can analyze a vast array of data from different sources, including sensors and imaging equipment used in agriculture. These algorithms, such as logistic regression models, ensemble methods, and decision trees, can reveal hidden patterns and relationships in data. In the context of plant monitoring, this means they can forecast future health conditions of plants by analyzing historical data and current observations. This approach is how machine learning aids in early health prediction in healthcare, as it helps in identifying risks early, so it is possible to take preemptive actions to mitigate health problems [7]. Similarly, computer vision, particularly using convolutional neural networks (CNNs) and recurrent neural networks (RNNs), plays a pivotal role in agriculture [8; 9]. CNNs can be particularly effective in processing and interpreting vast quantities of visual data from plants. They excel in tasks such as detecting signs of disease, nutrient deficiencies, or pest infestations by analyzing images of plant leaves and fruits [8]. Neural networks can enhance this by processing sequential data, making them suitable for analyzing temporal changes in plant conditions. This is analogous to their use in healthcare for analyzing medical images and time series data for early disease detection [7]. Combining these technologies, farmers and agriculturalists can make informed decisions, leading to improved crop health and yield, while also conserving resources. In the domain of IoT based plant monitoring in agriculture, deep learning can play a pivotal role, particularly in the analysis of plant data, which involves several steps. The initial step was to preprocess the data, which is essential to detect potential outliers, identify redundancies, and generate data for the training, validation, and testing of the machine learning algorithm. In a practical scenario, measurements from sensors can be combined with manually recorded data to create an augmented dataset. To ensure the quality and relevance of this dataset, approaches such as removing or not taking sensor measurements during certain periods (for example, at nighttime, in the case of indoor occupancy detection) can be applied to avoid bias due to unrepresentative data samples [10]. This approach results in a data set encompassing various parameters, such as temperature, humidity, light intensity, and CO\({}_{2}\) levels, which are crucial for analyzing plant health and environmental conditions [8]. The next step involves analyzing the relationships between different parameters in the dataset. For instance, in the case of indoor occupancy detection, a correlation analysis revealed that certain measurements, like CO\({}_{2}\) and Total Volatile Organic Compounds (TVOC), exhibited a strong correlation, indicating that one of these attributes could be eliminated without impacting the performance of the algorithm [10]. This type of analysis is crucial in plant monitoring as well, where factors such as temperature, relative humidity, and light intensity might exhibit dependencies. Understanding these relationships is vital for reducing the complexity of the model and focusing on the most impactful parameters for plant health analysis. Finally, a suitable deep learning algorithm can be selected to process and learn from the data. In the case of occupancy detection [10], a two-layer feedforward neural network (FNN) with sigmoid output neurons was used. The choice of such a network is motivated by its ability to learn and model various types of relationships, including non-linear and complex ones.