Abstract
In recent years, weeds are responsible for agricultural losses. To get rid of this problem, the farmers have to uniformly spray the whole field with the weedicides which require a huge quantity of weedicides. The process of spraying weedicides affects the environment. Weed detection in dense culture is a plant science problem that is important for field robotics where the detection of weed is currently a challenge so that the use of phytochemical products on crops can be reduced. To control and prevent specific weeds, a method of detecting the weed is presented in this paper. By collecting the plants and weeds datasets which are grayscale images, data is divided into training, validation, and testing datasets and then transported to the convolutional neural network. Based on the knowledge gained by the model, it can detect the weeds among plants. Utilization of a pre-trained VGG16 model for weed detection in dense cultures demonstrated improved performance compared to state of the art without the need for large datasets and high computational power for training.
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Gupta, S. et al. (2021). Improving Weed Detection Using Deep Learning Techniques. In: Mahapatra, R.P., Panigrahi, B.K., Kaushik, B.K., Roy, S. (eds) Proceedings of 6th International Conference on Recent Trends in Computing. Lecture Notes in Networks and Systems, vol 177. Springer, Singapore. https://doi.org/10.1007/978-981-33-4501-0_16
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DOI: https://doi.org/10.1007/978-981-33-4501-0_16
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