# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 4 Discussion  
In this study, we extracted more distinct types of features from plant images to comprehensively characterize their visual properties. These included shape descriptors, distance transform-based measures, color histograms, and three types of texture features. The integration of these diverse feature sets aimed to provide a holistic representation of the plants' morphological structures, spatial distributions, color compositions, and textural patterns. This multifaceted approach facilitated a robust analysis and interpretation of the visual information encoded within the images. We developed a dataset comprising images of six distinct types of weeds or plants, with each category represented by 500 images. To train six types of classifiers, we employed varying subsets of this dataset, consisting of 5, 10, 20, 40, 80, and 160 images from each category, with the remaining images reserved for testing purposes. By systematically adjusting the size of the training dataset, we aimed to assess the impact of varying levels of training data on the classifier's performance. This methodology allowed us to evaluate the classifier's robustness and generalization ability across different training scenarios, thereby providing valuable insights into the effectiveness of our classification approach. Figure 11: The precision results using all features except HOG features by training image number and classifier method. With only 10 training images, the detection rate hovered around 83%. When we expanded our training dataset to include 20 to 40 images per weed category, we observed a substantial improvement in detection rates, reaching approximately 86-91%. This increase in training data facilitated a more comprehensive learning process for the classifier, enabling it to better discern subtle variations and nuances among different weed categories. Consequently, the classifier demonstrated enhanced performance and accuracy in distinguishing between weed species, underscoring the importance of sufficient but not too much training data in achieving robust classification outcomes. In the final experiment, where we employed 160 images per weed category and utilized all available features, we achieved a notable detection result of 94.56%. Therefore, although the results are encouraging, further validation with larger and more diverse test datasets is essential to comprehensively assess the classifier's robustness and reliability in real-world scenarios. Our computer vision-based approach is comparable to those of CNN-based neural network approaches, with the added advantage of requiring only approximately 50-200 images per weed category for training. This requirement stands in stark contrast to the 1000-2000 images typically needed to train neural networks effectively. With our method, the proportion of training images relative to the total dataset was reduced to around 5% or a maximum of 10%. This significant reduction in training data requirements underscores the efficiency and practicality of our approach, offering a compelling alternative for weed detection and classification tasks, particularly in resource-constrained environments. For example, [PERSON] [19] conducted a thorough analysis of various datasets, but none were specifically tailored for weed detection. The closest dataset they used was Veg200, which focuses on vegetables but lacks specific plant and weed datasets. The Veg200 dataset comprises 200 vegetable classes and contains 91,117 images, making it suitable for training a CNN. However, in agricultural settings where robots need to operate efficiently, there are typically only 5-10 different types of plants requiring attention. In such scenarios, providing a robot with 10-20 samples of each weed type should suffice. Therefore, our research aimed to develop a solution for weed detection that could learn plant features from a limited number of sample images. [PERSON] demonstrated the significance of fine-grained image analysis using deep learning.