Abstract
Monitoring crops and weeds is a major challenge in agriculture and food production today. Weeds compete directly with crops for moisture, nutrients, and sunlight. They therefore have a significant negative impact on crop yield if not sufficiently controlled. Weed detection and mapping is an essential step in weed control. Many existing research studies recognize the importance of remote sensing systems and machine learning algorithms in weed management. Deep learning approaches have shown good performance in many agriculture-related remote sensing tasks, such as plant classification, disease detection, etc. However, despite the success of these approaches, they still face many challenges such as high computation cost, the need of large labelled datasets, intra-class discrimination (in growing phase weeds and crops share many attributes similarity as color, texture, and shape), etc. This paper aims to show that the attention-based deep network is a promising approach to address the forementioned problems, in the context of weeds and crops recognition with drone system. The specific objective of this study was to investigate visual transformers (ViT) and apply them to plant classification in Unmanned Aerial Vehicles (UAV) images. Data were collected using a high-resolution camera mounted on a UAV, which was deployed in beet, parsley and spinach fields. The acquired data were augmented to build larger dataset, since ViT requires large sample sets for better performance, we also adopted the transfer learning strategy. Experiments were set out to assess the effect of training and validation dataset size, as well as the effect of increasing the test set while reducing the training set. The results show that with a small labeled training dataset, the ViT models outperform state-of-the-art models such as EfficientNet and ResNet. The results of this study are promising and show the potential of ViT to be applied to a wide range of remote sensing image analysis tasks.
Highlights
Introduction published maps and institutional affilAgriculture is at the heart of scientific evolution and innovation to face major challenges for achieving high yield production while protecting plants growth and quality to meet the anticipated demands on the market [1]
Convolutional Neural Network (CNN)-Based architectures, ResNet and EfficientNet were trained along the visual transformers (ViT)-B16 and ViT-B32 in order to compare their performance on our custum dataset comprising of 5 classes
We have showed that applied to our five class agricultural dataset for weed identification, the ViT B-16 architecture pre-trained on ImageNet dataset outperforms other architectures and is more robust to a varying number of samples in the dataset
Summary
Introduction published maps and institutional affilAgriculture is at the heart of scientific evolution and innovation to face major challenges for achieving high yield production while protecting plants growth and quality to meet the anticipated demands on the market [1]. Weeds are generally considered harmful to agricultural production [3]. They compete directly with crop plants for water, nutrients and sunlight [4]. If the rate of usage of herbicides remains the same, in the near future, weeds will become fully resistant to these products and eventually destroy the harvest [5]. This is why weed and crop control management is becoming an essential field of research nowadays [6]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
