Weed Detection Using Deep Learning: A Systematic Literature Review

Abstract

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.

Advancements and developments in the detection and control of invasive weeds: a global review of the current challenges and future opportunities

“…As a consequence, there is an increase in the challenges and time taken to produce detailed precision weed maps (Zou et al 2021). To attend to some of these issues, it has been suggested that the use of specialized models or machine learning techniques can provide further enhancement and detection efficiency (Murad et al 2023). In this respect, a range of techniques have been developed to assist in this area, with some examples including the use of (1) artificial intelligence-based image analysis (Aitkenhead et al 2003; Haq et al 2023); (2) deep learning systems and algorithms (such as artificial neutral networks, convolutional neutral networks, deep neutral networks) (Hasan et al 2021; Murad et al 2023); (3) image processing techniques (including clustering, generative adversarial networks, Hilbert transformation, histograms of gradients, linear iterative, local binary patterns) (Murad et al 2023; Nixon and Aguado 2019); and (4) machine learning systems and algorithms (such as adaptive boosting, artificial neutral networks, decision trees, k-nearest neighbor, and support vector machines) (Murad et al 2023).…”

Exploring Innovations and Challenges in The Study of Hadith in The Digital Era

“…The researchers have employed the Literature Review writing guidance utilized by (Murad et al, 2023). Three processes, as proposed by , were implemented to ensure the alignment of the acquired and assessed data with the study questions.…”

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

“…Deep Neural Networks (DNNs) extend the complexity, number of connections and hidden layers of Artificial Neural Networks (ANNs). A convolutional neural network (CNN), a type of DNN, assigns learnable weights and biases to different aspects and objects within input images to distinguish and classify objects, such as weeds [1]. Unlike traditional machine learning algorithms that require manual feature selection and classifier choice, deep learning algorithms automatically extract features through self-learning from errors.…”

“…Unlike traditional machine learning algorithms that require manual feature selection and classifier choice, deep learning algorithms automatically extract features through self-learning from errors. This automatic feature extraction sets deep learning apart from the broader field of machine learning [1,92,93]. To train and evaluate a deep CNN model, each input image undergoes a sequence of convolution layers with filters, followed by flattening, pooling layers and fully connected layers.…”

“…The result of competition for resources is reduced crop yields. Yield losses depend on factors, such as weed species, population density and relative time of emergence and distribution, as well as on the soil type, soil moisture levels, pH and fertility [1,2]. For decades, researchers and farmers have struggled to control weeds to overcome the thorny challenges they pose.…”