Abstract
Precision farming helps to achieve maintainable agriculture, with an objective of boosting agricultural products with minimal negative impact on the environment. This paper outlines a deep learning approach based on Single Shot multibox Detector (SSD) to classify and locate weeds in low-land rice precision farming. This approach is designed for post-emergence application of herbicide for weed control in lowland rice fields. The SSD uses VGG-16 deep learning-based network architecture to extract a feature map. The adoption of multiscale features and convolution filter enables the algorithm to have a considerable high accuracy even at varying resolutions. Using SSD to train the weed recognition model, an entire system accuracy of 86% was recorded. The algorithm also has a system sensitivity of 93% and a precision value of 84%. The trained SSD model had an accuracy of 99% for close-up high definition images. The results of the system performance evaluation showed that the trained model could be adopted on a real rice farm to help reduce herbicide wastage and improve rice production with low chemical usage.
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Olaniyi, O.M., Daniya, E., Abdullahi, I.M., Bala, J.A., Olanrewaju, E.A. (2021). Weed Recognition System for Low-Land Rice Precision Farming Using Deep Learning Approach. In: Masrour, T., Cherrafi, A., El Hassani, I. (eds) Artificial Intelligence and Industrial Applications. A2IA 2020. Advances in Intelligent Systems and Computing, vol 1193. Springer, Cham. https://doi.org/10.1007/978-3-030-51186-9_27
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