Implementation of CNN for Plant Identification using UAV Imagery

Abstract

Plants are the world's most significant resource since they are the only natural source of oxygen. Additionally, plants are considered crucial since they are the major source of energy for humanity and have nutritional, therapeutic, and other benefits. Image identification has become more prominent in this technology-driven world, where many innovations are happening in this sphere. Image processing techniques are increasingly being used by researchers to identify plants. The capacity of Convolutional Neural Networks (CNN) to transfer weights learned with huge standard datasets to tasks with smaller collections or more particular data has improved over time. Several applications are made for image identification using deep learning, and Machine Learning (ML) algorithms. Plant image identification is a prominent part of such. The plant image dataset of about 300 images collected by mobile phone and camera from different places in the natural scenes with nine species of different plants are deployed for training. A fivelayered convolution neural network (CNN) is applied for largescale plant classification in a natural environment. The proposed work claims a higher accuracy in plant identification based on experimental data. The model achieves the utmost recognition rate of 96% NU108 dataset and UAV images of NU101 have achieved an accuracy of 97.8%.

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“…Given its dependability, the created CDLSTM model was probably going to be able to predict future precipitation and temperature values with accuracy by Haq [27]. The UAV imagery-based plant identification with CNN is introduced and analysed with different methods in terms of accuracy in [28,29]. To handle assault and non-attack occurrences within the system, two types were suggested in [30].…”