# Leveraging Machine Learning for Weed Management and Crop Enhancement: Vineyard Flora Classification
## 2 Related Work
### Experimental Studies--Flora Classification  
Traditional methods of flora identification require considerable time and expertise. DL techniques can expedite this process by analyzing real-world data and providing faster and more accurate results [21]. Also, irrigation planning is crucial in agriculture for maintaining constant yield and reducing water shortage risks, as it accounts for a significant portion of total water use. Some studies, like [PERSON] et al. [22] propose the development of an automatic irrigation system using advanced technologies, specifically leveraging deep learning algorithms. This study employs a Deep Q-Network (DQN) for optimizing irrigation scheduling in a tomato field in Portugal. The DQN agent interacts with two Long Short Term Memory (LSTM) models, serving as the environment. One LSTM model predicts the next day's total water content in the soil, while the other estimates crop yield based on seasonal environmental conditions, calculating the net return. The agent uses this information to determine the necessary irrigation amount. During training, three neural network architectures--Artificial Neural Network (ANN), LSTM, and Convolutional Neural Network (CNN)--are utilized to estimate the Q-table, a critical component in reinforcement learning. Notably, the study observes that, unlike the LSTM model, both the ANN and CNN struggle to accurately estimate the Q-table, resulting in a reduction in the agent's reward during training. The trained DQN model exhibits promising results, enhancing productivity by 11% and reducing water consumption by 20-30% compared to a traditional method. [PERSON] et al. [3] developed a CNN for plant species identification in colored photos, utilizing six datasets containing images from controlled and natural environments with varying lighting. The training set underwent augmentation and preprocessing, including excessive green segmentation. The overall accuracy of the model averaged 86.2%. Classes with more species generally exhibited better accuracy, highlighting challenges faced by classes with limited photo samples. [PERSON] et al. [23] developed an algorithm for the real-time segmentation and classification of plants using CNNs. Four CNN models (LeNet, AlexNet, cNet, and sNet) were employed to differentiate maize plants from weeds using RGB (red, green, blue) and Near-Infrared (NIR) images obtained from a multispectral camera. The images were processed to enhance green color detection and then converted to grayscale to distinguish plants from non-plant features. The cNet model with 16 filters showed the best performance, achieving a training accuracy of 97.23%. [PERSON] et al. [24] proposed using a hyperspectral NIR snapshot camera to classify weeds and maize. They identified critical spectral wavelengths and characteristics for classification using the Normalized Difference Vegetation Index (NDVI) and Radar Vegetation Index (RVI). ML techniques, including feature engineering and image processing, were applied to construct a Random Forest (RF) model. The RF model achieved high recalls, 100% for maize, 78.9% for _Convolulus arreusis_, 69.1% for _Runex_, and 75.2% for _Crisium arreuse_, and high accuracies and F1 scores for differentiating crops and weeds. [PERSON] and [PERSON] [25] developed a classification system for sugar beet crops and four weed types using shape features and image segmentation techniques. They used support vector machine (SVM) and artificial neural network (ANN) classifiers. For sugar beet classification, SVM achieved 96.67% accuracy and ANN achieved 93.33%. For weed classification, SVM and ANN both attained 92.50% accuracy. The shape-based weed detection algorithm successfully distinguished sugar beets and weeds, using red pixels for weeds, green pixels for sugar beets, and yellow pixels for misclassified areas. They also presented a visual representation of their model's outcomes.