# Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems
## 2 Related Work and Research Gaps
In agricultural plant monitoring, selecting the right algorithm is key to understand plant data from sensor networks. This data can show complex patterns influenced by environmental factors like soil moisture, relative humidity, and temperature, which are important for making more informed farming decisions. The research in [11] provides insights into the application of deep learning and computer vision in forecasting plant well-being in agriculture. The research focuses on using image processing and deep learning techniques to classify nutrient deficiencies in black gram plants, a methodology that can be extended to other crops for monitoring their health and predicting future well-being. Images of black gram plants grown under controlled conditions were used with specific nutrient treatments. Images were taken daily for 28 days, capturing both young and old leaves. However, not all days were represented due to issues like leaf maturity and lighting. To address the challenge of capturing the entire plant in a single image, the researchers combined images of old and young leaves. This approach not only enriched the data but also highlighted the importance of considering different plant parts in assessing overall health. They also used data augmentation techniques like horizontal flipping and scaling to expand the dataset, ensuring a robust model training process. The research in [11] utilized pre-trained deep learning models, particularly convolutional neural networks (CNNs), for feature extraction from the images. The use of a pre-trained model, such as ResNet50, allowed for efficient processing and learning from the images without the need for extensive computational resources. This approach demonstrates the effectiveness of leveraging existing models and adapting them for specific agricultural applications. The extracted features provide crucial information about the plant's health, enabling the detection of nutrient deficiencies that can affect the plant's well-being. For classification, the study employed techniques like multiclass logistic regression (MLR), support vector machine (SVM), and multilayer perceptron (MLP). These classifiers compared the performance in identifying various nutrient deficiencies, showing that deep learning models can effectively categorize different health states of plants based on visual cues. This classification capability is key to forecasting plant well-being, as it allows for the early detection of potential issues and timely intervention. For example, identifying a nitrogen deficiency early can lead to prompt fertilization, preventing yield loss and maintaining plant health. The research in [12] presents an approach in plant monitoring that can be applied in urban agriculture environments. It focuses on using infrared thermal imaging technology and a novel three-dimensional temperature (3D-3T) model for estimating canopy transpiration rates in plants, specifically citrus trees. This approach holds promise for enhancing plant monitoring and water management in urban agricultural settings. The research utilizes infrared thermal imaging to accurately measure the average temperature of plant canopies. This technology is key for urban agriculture, where environmental monitoring is crucial yet challenging due to the complex urban microclimates. By accurately capturing canopy temperatures, this method provides vital data for understanding plant water needs and stress levels, which are critical for optimizing irrigation and ensuring plant health in urban settings. The results presented in [12] accounts for the additional energy provided by solar radiation from different directions, a factor particularly relevant in urban environments where shading and light exposure can vary significantly due to buildings and other structures. The model's ability to estimate transpiration rates accurately, as validated by comparisons with sap flow measurements, makes it a useful tool for monitoring and managing water use in urban agriculture.