Article: Design and evaluation of a deep CNN algorithm for detecting farm weeds Journal: International Journal of Engineering Systems Modelling and Simulation (IJESMS) 2023 Vol.14 No.2 pp.71 - 79 Abstract: Weeds are unwanted plants that grow with crops and usually removed by spraying herbicides or by manual labour. Herbicides being sprayed mostly do not reach their target because of the focus on a very wide area. This also tends to harm the environment, and other living organisms. Manual labour is time-consuming and expensive and it is continuously managed and monitored. The autonomous robotics and image processing tasks can be completed with precision and ease in agriculture. With image processing, plants and weeds can be classified. Methods like scale invariant feature transforms (SIFT), speeded-up robust features (SURF), and ensemble learning, neural networks can be incorporated into identifying the difference. We can easily classify weeds and crops from images of plantations leveraging machine learning algorithms, artificial vision analysis systems, among others. Deep learning methods like convolutional neural network (CNN), rectified linear units (ReLU) and SoftMax (for classification) are focused in this paper. Inderscience Publishers - linking academia, business and industry through research

Title: Design and evaluation of a deep CNN algorithm for detecting farm weeds

Authors: Balachandra Pattanaik; Areej Malibari; M. Kumarasamy; V. Nagaraj; M. Gopikrishnan

Addresses: Department of Electrical and Computer Engineering, College of Engineering and Technology, Wollega University, Nekemte, Ethiopia ' Department of Computer Science, Faculty of Computing and Information Technology (FCIT), King Abdulaziz University, Jeddah, Saudi Arabia ' Department of Computer Science, College of Engineering and Technology, Wollega University, Nekemte, Ethiopia ' Department of Electronics and Communication Engineering, Knowledge Institute of Technology, Salem,Tamil Nadu, India ' Department of Computer Science and Engineering, Prathyusha Engineering College, Thiruvallur, Tamil Nadu, India

Abstract: Weeds are unwanted plants that grow with crops and usually removed by spraying herbicides or by manual labour. Herbicides being sprayed mostly do not reach their target because of the focus on a very wide area. This also tends to harm the environment, and other living organisms. Manual labour is time-consuming and expensive and it is continuously managed and monitored. The autonomous robotics and image processing tasks can be completed with precision and ease in agriculture. With image processing, plants and weeds can be classified. Methods like scale invariant feature transforms (SIFT), speeded-up robust features (SURF), and ensemble learning, neural networks can be incorporated into identifying the difference. We can easily classify weeds and crops from images of plantations leveraging machine learning algorithms, artificial vision analysis systems, among others. Deep learning methods like convolutional neural network (CNN), rectified linear units (ReLU) and SoftMax (for classification) are focused in this paper.

Keywords: image processing; deep learning; convolutional neural networks; CNN; rectified linear units; ReLU; weed detection; automation.

DOI: 10.1504/IJESMS.2023.129994

International Journal of Engineering Systems Modelling and Simulation, 2023 Vol.14 No.2, pp.71 - 79

Received: 29 Jun 2021
Accepted: 16 Aug 2021
Published online: 04 Apr 2023 *

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Title: Design and evaluation of a deep CNN algorithm for detecting farm weeds

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