# Leveraging Machine Learning for Weed Management and Crop Enhancement: Vineyard Flora Classification
## 2 Related Work
### Experimental Studies--Flora Classification
NIR, red, and NDVI images were used as input data for the CNN, with the model's probability output displaying crops in red, weeds in green, and the background in blue, compared against annotated ground truth. [PERSON] et al. [26] employed multispectral data captured by a Micro Aerial Vehicle (MAV) to classify sugar beetroot and weed species using CNN. They converted the data into SegNet format, categorizing images into crops, weeds, or both, using NDVI and image processing to differentiate vegetation. Six models with varying input channel sizes were trained and evaluated using F1 scores and AUC measures. The best model achieved an 80% average F1 score and accuracy on test data, showing promise in classifying crops and weeds despite dataset limitations. In a separate study, [PERSON] et al. [27] explored DL methods for hyperspectral image classification. They developed and refined four models: two-dimensional CNN (2-D-CNN), three-dimensional CNN (3-D-CNN), 2D CNN based on regions (R-2-D-CNN), and 3D CNN with a regional focus (R-3-D-CNN). These models considered both spectral and spatial factors. The R-2-D-CNN and R-3-D-CNN models achieved exceptional accuracy rates, with the R-3-D-CNN reaching 99.87% and 99.97% accuracy in one dataset, emphasizing the importance of spectral and spatial considerations in hyperspectral image classification. In a related study developed by [28], a DL technique using KERAS API and TensorFlow backend was employed for the picture classification of nine different crops and their corresponding weeds. The model achieved an impressive accuracy rate of 96.3% by correctly categorizing plants and weeds using 250 images of each plant type acquired in the field. The model's accuracy highlighted its effectiveness in distinguishing between crops and weeds for agricultural applications. [PERSON] et al. [29] developed a sophisticated algorithm to identify weeds in vegetable plantations. The algorithm utilized CenterNet, representing objects as single points with object centers predicted using a heatmap. Ground truth key points were transformed into smaller key-point heatmaps through a Gaussian kernel and focal loss for network training. Weeds were identified as green objects outside the annotated bounding boxes. The trained CenterNet model exhibited a very good performance with a precision of 95.6%, recall of 95.0%, and F1 score of 95.3%. It successfully identified vegetables under various conditions, presenting the final segmentation results with vegetable regions highlighted in red boxes. [PERSON] et al. [30] introduced a novel approach for classifying weeds in wheat photos taken by a drone. Three machine learning models, ANN, SVM, and the K-nearest neighbors' algorithm (KNN), were utilized. AlexNet was used with a binary optimizer to enhance feature selection. This approach achieved a detection accuracy of 97.70%, F1 score of 98.60%, specificity of 95.20%, and sensitivity of 98.40%, outperforming other methods. [PERSON] et al. [31] investigated weed detection using DL models (VGG16 and ResNet50) with varying backgrounds. Images of weeds and crops were captured with different backgrounds (uniform and non-uniform). The models showed better performance on images like their training backgrounds. A model trained with combined datasets achieved an F1 score between 92% and 99%. [PERSON] et al. [32] compared SVM and VGG16 DL classifiers using RGB images to classify weeds and crop species. Image processing techniques were employed to extract the green portion of the pictures, followed by feature extraction using Local Binary Pattern (LBP) and Gray-level Co-occurrence Matrix (GLCM) features. The VGG16 model outperformed SVM, with F1 score results ranging from 93% to 97.5%. Thus, advances in image processing, spectral analysis, and model development have contributed to more accurate and efficient methods for classifying plants and weeds.