Authors
Samy S. Abu-Naser
North Dakota State University (PhD)
Abstract
Agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Preceding instrument vision methods established for selective weeding have confronted with major challenges for trustworthy and precise weed recognition. In this paper, plant seedlings classification approach is presented with a dataset that contains approximately 5,000 images with 960 unique plants that belong to 12 species at a few developing phases. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results found that CNN-driven seedling classification applications when used in farming automation have the latent to enhance crop harvest and improve output and productivity when designed properly. The trained model achieved an accuracy of 99.48% on a held-out test set, demonstrating the feasibility of this approach.
Keywords Plant Seedlings  Classification  Deep Learning
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Predicting Whether a Couple is Going to Get Divorced or Not Using Artificial Neural Networks.Ibrahim M. Nasser - 2019 - International Journal of Engineering and Information Systems (IJEAIS) 3 (10):49-55.
ANN for Tic-Tac-Toe Learning.Dalffa Abu-Mohaned - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 3 (2):9-17.
Apple Fruits Classification Using Deep Learning.Shawwa Mohammad - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):1-6.
Classification of Alzheimer’s Disease Using Traditional Classifiers with Pre-Trained CNN.Husam R. Almadhoun & Samy S. Abu-Naser - 2021 - International Journal of Academic Health and Medical Research (IJAHMR) 5 (4):17-21.

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