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

Ildar Rakhmatulin,Christian Andreasen,Andreas Kamilaris

doi:10.3390/rs13214486

Ildar Rakhmatulin, Christian Andreasen + Show 1 more

Open Access

https://doi.org/10.3390/rs13214486

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Journal: Remote Sensing	Publication Date: Nov 8, 2021
Citations: 39	License type: CC BY 4.0

Affiliation: University of Copenhagen, University of Twente

Abstract

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.

Highlights

Weeds constitute one of the most devastating constraints for crop production, and efficient weed control is a prerequisite for increasing crop yield and food production for a growing world population [1]
This paper performed a survey on the state-of-the-art methods used for weed identification in agricultural fields, which robots can use for effective weed control
Various methods and techniques were reviewed, focusing on shallow neural networks and deep learning (DL), and mentioning different approaches from machine vision that have been applied in the field

Summary

Introduction

Weeds constitute one of the most devastating constraints for crop production, and efficient weed control is a prerequisite for increasing crop yield and food production for a growing world population [1]. Weed control may negatively affect the environment [2]. The application of herbicides may result in pollution of the environment because, in most cases, only a tiny proportion of the applied chemicals hits the targets while most herbicides hit the ground, and a part of them may drift away [2,3]. Mechanical weed control may result in erosion and harm beneficial organisms such as earthworms in the soil and spiders on the soil surface [4,5]. Other weed control methods have other disadvantages and often affect the environment negatively. Sustainable weed control methods need to be designed only to affect the weed plants and interfere as little as possible with the surroundings. Weed control could be improved and be more sustainable if weeds were identified and located in real-time before applying any control methods

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