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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 i...
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The detection and mapping of winter wheat and the canopy cover of associated weeds, such as chickweed and hairy buttercup, are essential for crop and weed management. With emerging drone technologies, the use of a multispectral camera with the red-edge band, such as Altum, is commonly used for crop and weed mapping. However, little is understood ab...
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... Despite the reported decrease in herbicide application using precision agriculture [24], few studies have explored weed detection under field conditions. A systematic review has reported that just 34 weed species have been targeted using weed detection models, frequently focusing on a single weed-crop species association [30], which does not always reflect the conditions observed at a commercial farm. Datasets enabling precision agriculture technologies are typically collected using hand held devices [31], vehicle mounted camera systems, or UAVs [25,26,[32][33][34]. ...
Weeds pose a significant threat to agricultural production, leading to substantial yield losses and increased herbicide usage, with severe economic and environmental implications. This paper uses deep learning to explore a novel approach via targeted segmentation mapping of crop plants rather than weeds, focusing on canola (Brassica napus) as the target crop. Multiple deep learning architectures (ResNet-18, ResNet-34, and VGG-16) were trained for the pixel-wise segmentation of canola plants in the presence of other plant species, assuming all non-canola plants are weeds. Three distinct datasets (T1_miling, T2_miling, and YC) containing 3799 images of canola plants in varying field conditions alongside other plant species were collected with handheld devices at 1.5 m. The top performing model, ResNet-34, achieved an average precision of 0.84, a recall of 0.87, a Jaccard index (IoU) of 0.77, and a Macro F1 score of 0.85, with some variations between datasets. This approach offers increased feature variety for model learning, making it applicable to the identification of a wide range of weed species growing among canola plants, without the need for separate weed datasets. Furthermore, it highlights the importance of accounting for the growth stage and positioning of plants in field conditions when developing weed detection models. The study contributes to the growing field of precision agriculture and offers a promising alternative strategy for weed detection in diverse field environments, with implications for the development of innovative weed control techniques.
... The current literature focuses on specific tasks or algorithms, such as logistics [6][7][8], trajectory optimization [9], object detection [10], agricultural operations [11,12] and inventory [13]. A wide range of notable contributions in this field are explored, from the application of DL algorithms for real time object detection and recognition to the use of massive data processing techniques for dynamic trajectory optimization. ...
... Pollination, essential for the production of some foods, is supported by drones equipped with AI algorithms that are capable of identifying unpollinated flowers and, if necessary, carrying out artificial pollination [11]. Another of the most significant techniques in this area is the detection and control of floods caused by weeds [12]. Using algorithms like SVM [44], RF [45] and KNN [46], drones can identify flood prone areas and precisely eradicate weeds, obtaining a guarantee of agricultural production [47]. ...
... The SVM method developed by Cortes and Vapnik in 1995 is a widely used machine learning technique for data classification problems [46,47]. The SVM has the ability to make more accurate classifications than other classifiers [48,49]. The underlying concept of SVM data classification is to obtain an optimal hyperplane separator that can linearly separate the classification problems [50,51]. ...
Excessive consumption of non-green products contributes to increasing levels of environmental damage. One effort to overcome this problem is to influence consumer behavior in a more environment-friendly direction. Therefore, it is necessary to identify factors that influence environment-green consumer behavior. The main aim of this research is to build a more comprehensive consumer behavior model inductively using artificial intelligence algorithms. This research aims to identify factors based on questionnaire instruments and interviews as data collection methods. Questionnaires were distributed to the public in the province of West Java, Indonesia, where only 253 respondents gave valid answers. This research measures stated behavior and not actual behavior. This research focuses on variables that influence environment-green consumer behavior, including environmental knowledge, environmental concern, health concern (HC), attitude toward behavior (ATB), subjective norm (SN), perceived price (PP), perceived value and quality, and green consumer behavior (GCB). All variables were validated using the partial least square-structural equation model method using SmartPLS 4.0 software. Furthermore, the validated variables were modeled and classified using the support vector machine (SVM) method. The test results show that all variables, both direct and indirect, have a positive and significant influence on environment-green consumer behavior, with a significant level of p < 0.05 and t > 1.96. The SVM modeling predictions reach a high level of accuracy of about 96%. This means that the variables ATB, SN, and PVC have a direct effect on GCB. Additionally, most respondents fell into the “less environment-green behavior” classification, indicating there is a space for improvement in promoting environment-green choices.
... There are several studies on the use of CNN for plant identification using UAV imagery. Murad et al. [41] presented a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. The review identified 11 DL algorithms with 19 different variants of CNNs for weed detection. ...
Egypt’s agricultural sector plays a critical role in the country’s economy, with wheat cultivation being vital for ensuring food security. However, the challenges faced by wheat farming in Egypt, such as climate change, water scarcity, and pest infestations, contribute to yield fluctuations, highlighting the need for accurate and timely predictions of wheat productivity. To address this need, time series analysis is employed, which involves analyzing data collected at regular intervals over a specific time frame. Time series data can encompass multiple variables recorded simultaneously at each interval, known as multivariate time series data. Traditional statistical methods have been commonly used for time series analysis. However, these methods have limitations when dealing with nonlinear and complex data patterns, especially in agricultural data exhibiting spatiotemporal characteristics. Deep learning techniques have emerged as a promising solution to address these limitations. In this paper, we have developed the multivariate multi-head attention temporal convolutional networks (MATCN) model specifically to handle the spatiotemporal nature of multivariate time series data in the context of wheat crop productivity prediction in Egypt. The experimental results revealed that our proposed MATCN model exhibited the most effective prediction performance when compared to other state-of-the-art models, achieving a mean absolute percentage error (MAPE) values ranging from 0.091 to 5.5%.
... 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. ...
... 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. ...
... 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. ...
Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To achieve precise crop identification, it is essential to employ suitable sensors and optimized algorithms. Deep learning plays a crucial role in enhancing weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, effectively reducing the overall cost of agricultural production. This paper provides a thorough overview of the application of deep learning for crop and weed recognition in smart agricultural equipment. Starting with an overview of intelligent agricultural tools, sensors and identification algorithms, the discussion delves into instructive examples, showcasing the technology’s prowess in distinguishing between weeds and crops. The narrative highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing prospects. By marrying cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture.
... In addition, similar studies have also been conducted in weed detection [131], chili biomass calculation, pod detection and counting, etc. Longzhe Quan[132] developed a dual stream dense feature fusion convolutional neural network model based on RGB-D to achieve weed detection and aboveground fresh weight calculation in land parcels, obtaining richer information than RGB images. By constructing a NiN-Block structural module to enhance feature extraction and fusion, the average accuracy of predicting weed fresh weight reached 75.34% when IoU was set to 0.5; Taewon Moon [133] combined simple formulas with deep learning networks to calculate the fresh weight and leaf area of greenhouse sweet peppers. ...
Yield calculation is an important link in modern precision agriculture, which is an effective means to improve breeding efficiency, and adjust planting and marketing plans. With the continuous progress of artificial intelligence and sensing technology, yield calculation schemes based on image processing technology have many advantages such as high accuracy, low cost, and non-destructive calculation, and have been favored by a large number of researchers. This article reviews the research progress of crop yield calculation based on remote sensing images and visible light images, describes the technical characteristics and applicable objects of different schemes, and focuses on detailed explanations of data acquisition, independent variable screening, algorithm selection and optimization. Common issues are also discussed and summarized. Finally, solutions are proposed for the main problems that have arisen so far, and future research directions are predicted, with the aim of achieving more progress and wider popularization of yield calculation solutions based on image technology.
... Its success is attributable to technological advances that have exponentially expanded processing capacity, allowing for the training of sophisticated neural networks [2]. This increase in processing capacity, combined with lower hardware prices, has democratized deep learning, accelerating its application across areas such as healthcare [3], agrifood [4], finance, and transportation [5]. ...
The integration of deep learning in healthcare has propelled advancements in diagnostics and decision support. However, the inherent opacity of deep neural networks (DNNs) poses challenges to their acceptance and trust in clinical settings. This survey paper delves into the landscape of explainable deep learning techniques within the healthcare domain, offering a thorough examination of deep learning explainability techniques. Recognizing the pressing need for nuanced interpretability, we extend our focus to include the integration of fuzzy logic as a novel and vital category. The survey begins by categorizing and critically analyzing existing intrinsic, visualization, and distillation techniques, shedding light on their strengths and limitations in healthcare applications. Building upon this foundation, we introduce fuzzy logic as a distinct category, emphasizing its capacity to address uncertainties inherent in medical data, thus contributing to the interpretability of DNNs. Fuzzy logic, traditionally applied in decision-making contexts, offers a unique perspective on unraveling the black box of DNNs, providing a structured framework for capturing and explaining complex decision processes. Through a comprehensive exploration of techniques, we showcase the effectiveness of fuzzy logic as an additional layer of interpretability, complementing intrinsic, visualization, and distillation methods. Our survey contributes to a holistic understanding of explainable deep learning in healthcare, facilitating the seamless integration of DNNs into clinical workflows. By combining traditional methods with the novel inclusion of fuzzy logic, we aim to provide a nuanced and comprehensive view of interpretability techniques, advancing the transparency and trustworthiness of deep learning models in the healthcare landscape.
To effectively solve the increasingly complex problems experienced by human beings, the latest development trend is to apply a large number of different types of sensors to collect data in order to establish effective solutions based on deep learning and artificial intelligence [...]
