# Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network
###### Abstract
It features a distinctive encoder-decoder structure that is symmetrical, comprising a contracting path (encoder) for feature extraction and an expansive path (decoder) for precise localization. The contracting path consists of repeated applications of convolutional layers followed by max pooling, while the expansive path employs transposed convolutions to upsample the feature maps. It has been successfully employed in tasks such as satellite image segmentation and agricultural monitoring. Despite its strengths, U-Net can struggle with precise boundary delineation in complex images, especially where objects have complex textures or overlap significantly. Therefore, the integration of attention mechanisms in the proposed MSCNN allows the model to focus selectively on important features within the input image. This capability enhances the model's ability to delineate object boundaries more accurately compared to U-Net, which may struggle in complex scenarios with overlapping objects. SegNet, developed by [PERSON] et al. [11], is a deep convolutional encoder-decoder architecture specifically designed for semantic segmentation tasks. SegNet has made contributions to semantic segmentation through its encoder-decoder architecture and upsampling techniques. However, several challenges remain, including boundary precision, performance on small objects, and limited generalization. These issues underscore the need for further research and improvement in these areas. The proposed MSCNNs utilize multi-scale feature extraction, which allows the network to capture details at various resolutions. This capability is crucial for accurately delineating object boundaries, particularly in complex images where SegNet may falter due to its reliance on max-pooling  
Figure 1: Vegetation cover segmentation. (**a**) Input image, (**b**) percentage of vegetation cover marked in yellow (input image obtained from [4]). indices for upsampling. By integrating attention mechanisms, the proposed MSCNN can focus on relevant features contributing to boundary accuracy, leading to improved segmentation results. [PERSON] et al. [12] introduced a novel approach utilizing a deep convolutional neural network (DCNN) for the segmentation of damaged vegetation from RGB images, leveraging virtual Near Infrared channel (NIR-channel) estimation. The accuracy of this segmentation is significantly dependent on the reliability of the virtual NIR-channel estimation. If the estimation process is flawed or fails to accurately represent the true NIR characteristics, it may result in suboptimal segmentation performance, potentially obscuring critical details essential for effective vegetation analysis. Current convolutional neural networks (CNNs) have shown impressive and efficient performance in many areas of earth observation and object recognition [13; 14]. However, the fixed kernel size employed in a convolution operation in CNN may limit the capability of feature extraction and may affect the ability to represent multiple scales. [PERSON] et al. [15] introduced the geometric convolutional neural network (G-CNN) for multi-scale remote sensing image segmentation. Their method captures features at different scales, enabling the model to discern both details and contextual information. Moreover, they incorporate geometric information into the convolutional framework. Despite its effectiveness, the performance of the G-CNN model heavily relies on the availability of high-quality, labeled datasets for training. In many agricultural regions, obtaining such datasets can be challenging, which may limit the model's applicability and effectiveness. On the other hand, Long Short-Term Memory (LSTM) networks, a sophisticated variant of recurrent neural network (RNN) architecture, are particularly well suited for modeling sequential data and capturing long-term dependencies. In the study by [PERSON] et al. [16], a modified bidirectional LSTM (BiLSTM) neural network is proposed for rail vehicle suspension fault detection.