Progress in a Development of a Laser-Based Weed Control System

Sirikunkitti, Sirinun; Chongcharoen, K; Yoongsuntia, Prajuab; Ratanavis, Amarin

doi:10.1109/ri2c48728.2019.8999953

Cited by 5 publications

(1 citation statement)

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“…A drawback is that this work requires a lot of time. Additionally, several works have focused on laser technology for weed control in ideal imaging conditions, without much emphasis on the use of machine vision in real-world fields [87][88][89][90][91][92][93][94]. Raja et al [86] proposed the marking of the crops, making them machine-readable for robotic systems.…”

Section: Available Weed Detection Systemsmentioning

confidence: 99%

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

2021

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Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pre-trained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized.

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