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Shahi, T.B.; Dahal, S.; Sitaula, C.; Neupane, A.; Guo, W. Deep Learning-Based Weed Detection Using UAV Images. Encyclopedia. Available online: https://encyclopedia.pub/entry/50236 (accessed on 03 July 2024).
Shahi TB, Dahal S, Sitaula C, Neupane A, Guo W. Deep Learning-Based Weed Detection Using UAV Images. Encyclopedia. Available at: https://encyclopedia.pub/entry/50236. Accessed July 03, 2024.
Shahi, Tej Bahadur, Sweekar Dahal, Chiranjibi Sitaula, Arjun Neupane, William Guo. "Deep Learning-Based Weed Detection Using UAV Images" Encyclopedia, https://encyclopedia.pub/entry/50236 (accessed July 03, 2024).
Shahi, T.B., Dahal, S., Sitaula, C., Neupane, A., & Guo, W. (2023, October 13). Deep Learning-Based Weed Detection Using UAV Images. In Encyclopedia. https://encyclopedia.pub/entry/50236
Shahi, Tej Bahadur, et al. "Deep Learning-Based Weed Detection Using UAV Images." Encyclopedia. Web. 13 October, 2023.
Deep Learning-Based Weed Detection Using UAV Images
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This entry is adapted from the peer-reviewed paper 10.3390/drones7100624

Deep learning-based weed detection using UAV images. Recently, the Unmanned Aerial Vehicle (UAV) has made significant progress in its design and capability, including payload flexibility, communication and connectivity, navigation and autonomy, speed and flight time, which has potential to revolutionize the precision agriculture.

semantic segmentation UAV drones deep learning weed detection precision agriculture

1. Introduction

Global food demand is projected to surge by a significant margin of 35% to 56% from 2010 to 2050 [1]. However, the expansion of industrialization, desertification and urbanization has led to a reduction in the crop production area and, hence, productivity of food [2]. In addition to these challenges, climate change is increasingly creating favorable conditions for pests such as insects and weeds, harming crops [3]. Therefore, crop quality and quantity will be affected by insects and weeds if the appropriate treatment is not devised in a timely manner. Traditionally, herbicides and pesticides have been employed as a means of control [4]. When these herbicides are sprayed throughout entire fields without making precise identification of weeds, such an application of herbicides, while serving its purpose, results in a detrimental impact on both crop yield and the environment. While they effectively combat pests and diseases that threaten crops, their use can lead to reduced agricultural productivity due to their excessive use where no weeds are present [5]. Therefore, it is essential to precisely identify the weeds vs. crops, so that cultivated plants can be saved from pesticide harm. As such, there is a requirement for a method of weed management that can gather and assess weed-related data within the agricultural field, while also taking appropriate measures to effectively regulate weed growth on farms [6].
Remote sensing (RS)-based approaches can be an alternative for automated weed detection using satellite imagery [7]. However, the success of satellite-based RS in weed detection is significantly influenced by three major limitations. First, the satellites acquire images with spatial resolutions measured in meters (e.g., Landsat at 30 m and Sentinel at 10 m), which is generally insufficient for analyzing plant- or individual plot-level weed data. Moreover, the fixed schedule of satellite revisits may not align with the timing needed to capture essential crop field images. Additionally, environmental factors like cloud cover frequently hinder the dependable quality of these images.
Recently, the Unmanned Aerial Vehicle (UAV) has made significant progress in its design and capability, including payload flexibility, communication and connectivity, navigation and autonomy, speed and flight time, etc. [8]. It offers versatile revisiting capabilities, allowing farmers/researchers to deploy it when weather conditions permit, ensuring frequent image capture (thus achieving high temporal resolution). Moreover, UAVs can capture images with remarkable spatial detail, closely observing individual plants from an elevated perspective, leading to centimeter-level image resolutions. Additionally, by flying at lower altitudes, UAVs can bypass cloud cover, obtaining clear and high-quality images [9]. Combined with the high-resolution crop field images acquired with UAV, semantic segmentation methods based on deep learning (DL) can provide a promising method for precise weed detection.
Semantic segmentation (SS) in computer vision is a pixel-level classification task that has revolutionized various fields, such as medical image segmentation [10][11] and precision agriculture (PA) [12]. For instance, Liu et al. [11] utilized the segmentation of retinal images to help diagnose and treat retinal diseases. In the PA domain, SS has been adopted for different problems such as agricultural field boundary segmentation [12], agricultural land segmentation [13], diseased vs. healthy plant detection [14] and weed segmentation [15]. Weed segmentation, which helps to identify unnecessary plants disturbing the growth of crops, is considered one of the major areas that directly contribute to improving crop productivity.
Over recent years, SS has gained significant attraction in the weed detection area of PA. Computer vision techniques that utilize image processing and machine learning methods for weed detection are widely investigated in the literature [16][17][18]. However, deep learning methods for SS have shown state-of-the-art (SOTA) results for image segmentation tasks in general. The availability of pre-trained deep neural networks on large datasets such as ImageNet [19] made it possible to transfer cross-domain knowledge to agriculture field images. For instance, convolutional neural networks (CNNs) such as DeepLab [20], UNet [21] and SegNet [22] are implemented for weed detection on various crop fields. The performance of these neural networks depends on multiple factors, such as the resolution of images, types of crops and field conditions. Since the colour and texture of weeds are very similar to crops, it is a complex problem to make a differentiation between crops and weeds. Furthermore, if more than one type of weed is present in the field, it becomes more challenging to segment such regions.

2. Deep Learning-Based Weed Detection Using UAV Images

Owing to the recent advancement in drone and sensor technology, the research on weed detection using DL methods has been swiftly progressing. For instance, a CNN was implemented by Dos et al. [15] for weed detection using aerial images. They acquired soybean (Glycine max) field images in Brazil with a drone and created a database of more than 1500 images including images of the soil, soybeans, broad-leaf and grass weeds. A classification accuracy of 98% was achieved using ConvNet while detecting the broadleaf and grass weeds. However, their approach employed the classification of whole images into different categories for weed detection rather than the segmentation of image pixels into various classes. Similarly, a CNN was implemented for weed mapping in Sod production using aerial images by Zhang et al. [23]. They first processed the UAV images using Pix4DMapper and produced an orthomosaic of the agricultural field. Then, the orthomosaic was divided into smaller image tiles. A CNN was built with an image size of (125px×125px) as input. The CNN achieved a maximum precision of 0.87, 0.82, 0.83, 0.90 and 0.88 for broadleaf, grass weeds, spurge (Euphorbia spp.), sedges (Cyperus spp.) and no weeds, respectively. Ong et al. [24] performed weed detection on a Chinese cabbage field using UAV images. They adapted AlexNet [25] to perform weed detection and compared its performance with traditional machine learning classifiers such as Random forest [26]. The results showed that CNN achieved the highest accuracy of 92.41%, which was 6% higher than that of Random forest. A lightweight deep learning framework for weed detection in soybean fields was implemented by Razfar et al. [27] using MobileNetV2 [28] and ResNet50 [29] networks.
Aside from the single-stage CNNs, few works have been reported that use multi-stage pipelines for weed detection on UAV images. For instance, Bah et al. [30] implemented a three-step method for weed detection on spinach and bean fields using UAV images. First, they detected the crop rows using the Hough transform [31] technique; then, the weeds between these crop rows were used as training samples where a CNN was trained to detect the crop and weed in the UAV images. However, their proposal depends on the accuracy of the line detection technique, which might not be robust when the UAV images contain varying backgrounds and image contrast. A two-stage classifier for weed detection in tobacco crops was implemented in [32]. Here, they first segmented the background pixel from the vegetation pixels which included both weed as well as tobacco pixels. Then, a three-class image segmentation model was implemented. Their proposal achieved the maximum Intersection of Union (IoU) of 0.91 for weed segmentation. However, the two-stage segmentation model requires separate training at each stage, and hence it is not possible to train the model in an end-to-end fashion, adding extra complexity to its deployment.
Object detection approaches such as single shot detector (SSD) [33], Faster RCNN [34] and YOLO [35] were also employed for weed detection using UAV images. For instance, Veeranampalayam et al. [33] compared two object detectors, namely, Faster RCNN and SSD, for weed detection using UAV images. The InceptionV2 [36] model was used for feature extraction in both detectors (Faster RCNN and SSD). The comparison revealed that Faster RCNN models produced a higher accuracy as well as less inference time for weed detection.
The segmentation of images into weed and non-weed regions at the pixel level is more precise and can be beneficial for the accurate application of pesticides. Xu et al. [20] combined the visible color index with a DL-based segmentation model for weed detection in soybean fields. They first generated the visible color index image for each UAV image and fed it into a DL-based segmentation model which utilized the DeeplabV3 [37] network. When comparing its performance with other SOTA segmentation architectures such as fully convolutional neural network (FCNN) [38] and UNet [39], it provided an accuracy of 90.50% and an IoU score of 95.90% for weed segmentation.

References

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Subjects: Remote Sensing
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Tej Bahadur Shahi
,
Sweekar Dahal
,
Chiranjibi Sitaula
,
Arjun Neupane
,
William Guo
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Update Date: 13 Oct 2023
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