"artificial intelligence" AND Plant

Deep Neural Networks (DNNs) are known to be vulnerable to both backdoor and adversarial attacks. In the literature, these two types of attacks are commonly treated as distinct robustness problems and solved separately, since they belong to training-time and inference-time attacks respectively. However, this paper revealed that there is an intriguing connection between them: (1) planting a backdoor into a model will significantly affect the model's adversarial examples; (2) for an infected model, its adversarial examples have similar features as the triggered images...

The development of non-invasive methods and accessible tools for application to plant phenotyping is considered a breakthrough. This work presents the preliminary results using an electronic nose (E-Nose) and machine learning (ML) as affordable tools. An E-Nose is an electronic system used for smell global analysis, which emulates the human nose structure. The soybean (Glycine Max) was used to conduct this experiment under water stress. Commercial E-Nose was used, and a chamber was designed and built to conduct the measurement of the gas sample from the soybean...

Microplastics (MPs) pollution is an emerging environmental health concern, impacting soil, plants, animals, and humans through their entry into the food chain via bioaccumulation. Human activities such as improper solid waste dumping are significant sources that ultimately transport MPs into the water bodies of the coastal areas. Moreover, there is a complex interplay between the coastal climate dynamics, environmental factors, the burgeoning issue of MPs pollution and the complex web of coastal pollution. We embark on a comprehensive journey, synthesizing the latest research across multiple disciplines to provide a holistic understanding of how these inter-connected factors shape and reshape the coastal ecosystems...

Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate. Enviromics-based integration of statistics, envirotyping (i.e., classifying environmental factors), and remote sensing could help unravel the complex interplay of genetics, environment, and management (G × E × M)...

Personalized traditional Chinese medicine(TCM) preparations have entered a stage of rapid development. The key to the healthy development of this industry is to establish a sound manufacturing standard and quality control system. This paper analyzed the characteristics of personalized TCM preparations and drew reference from the quality management standards in the production of commissioned decoctions and oral pastes, on the basis of which the production quality management scheme and cautions for the safe production of personalized TCM preparations was put forward with consideration to various problems that may exist and occur in the production of such preparations...

In recent years, as people's living standards continue to improve, and the pace of life accelerates dramatically, the demand and quality of traditional Chinese medicine(TCM) services from patients continue to rise. As an essential supplement to the existing forms of TCM application, such as Chinese patent medicine, decoction, and formulated granules, presonalized TCM preparations is facing an increasing market demand. Currently, manual and semi-mechanized production are the primary production ways in presonalized TCM preparations...

Pharmacognosy from medicinal plants involves the scientific domain of medicinal compounding based on their medicinal properties. Accurate identification of medicinal plants is crucial, especially by examining their leaves. Choosing the wrong plant species for medicinal preparations can have adverse side effects. This study presents a Human-Centered Artificial Intelligence approach for medicinal plant identification, combining a YOLOv7-based Leaf Localizer with a leaf Class Verifier based on DenseNet through a Confidence Score Analyser algorithm...

This study utilizes neural networks to detect and locate thermal anomalies in low-pressure steam turbines, some of which experienced a drop in efficiency. Standard approaches relying on expert knowledge or statistical methods struggled to identify the anomalous steam line due to difficulty in capturing nonlinear and weak relations in the presence of linear and strong ones. In this research, some inputs that linearly relate to outputs have been intentionally neglected. The remaining inputs have been used to train shallow feedforward or long short-term memory neural networks using measured data...

Outdoor pipeline leaks are difficult to accurately measure using existing concentration measurement systems installed in petrochemical plants owing to external air currents. Besides, leak detection is only possible for a specific gas. The purpose of this study was to develop an image/ultrasonic convergence camera system that incorporates artificial intelligence (AI) to improve pipe leak detection and establish a real-time monitoring system. Our system includes an advanced ultrasonic camera coupled with a deep learning-based object-detection algorithm trained on pipe image data from petrochemical plants...

Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities...

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The research of plant seeds has always been a focus of agricultural and forestry research, and seed identification is an indispensable part of it. With the continuous application of artificial intelligence technology in the field of agriculture, seed identification through computer vision can effectively promote the development of agricultural and forestry wisdom. Data is the foundation of computer vision, but there is a lack of suitable datasets in the agricultural field. In this paper, a seed dataset named LZUPSD is established...

Biogas production through anaerobic digestion (AD) is one of the complex non-linear biological processes, wherein understanding its dynamics plays a crucial role towards process control and optimization. In this work, a machine learning based biogas predictive model was developed for high solid systems using algorithms, including SVM, ET, DT, GPR, and KNN and two different datasets (Dataset-1:10, Dataset-2:5 inputs). Support Vector Machine had the highest accuracy (R2 ) of all the algorithms at 91 % (Dataset-1) and 87 % (Dataset-2), respectively...

The two-spotted spider mite (TSSM), Tetranychus urticae, is among the most destructive piercing-sucking herbivores, infesting more than 1100 plant species, including numerous greenhouse and open-field crops of significant economic importance. Its prolific fecundity and short life cycle contribute to the development of resistance to pesticides. However, effective resistance loci in plants are still unknown. To advance research on plant-mite interactions and identify genes contributing to plant immunity against TSSM, efficient methods are required to screen large, genetically diverse populations...

Deep learning and computer vision, using remote sensing and drones, are 2 promising nondestructive methods for plant monitoring and phenotyping. However, their applications are infeasible for many crop systems under tree canopies, such as coffee crops, making it challenging to perform plant monitoring and phenotyping at a large spatial scale at a low cost. This study aims to develop a geographic-scale monitoring method for coffee cherry counting, supported by an artificial intelligence (AI)-powered citizen science approach...

BACKGROUND: Halyomorpha halys is one of the most damaging invasive agricultural pests in north America and southern Europe. It is commonly monitored using pheromone traps, which are not very effective as few bugs are caught, some escape and/or remain outside the trap on surrounding plants where they feed, increasing the damage. Other monitoring techniques are based on visual sampling, sweep netting and tree-beating. However, all these methods require several hours of human labour and are difficult to apply to large areas...

This work introduces a novel approach to remotely count and monitor potato plants in high-altitude regions of India using an unmanned aerial vehicle (UAV) and an artificial intelligence (AI)-based deep learning (DL) network. The proposed methodology involves the use of a self-created AI model called PlantSegNet, which is based on VGG-16 and U-Net architectures, to analyze aerial RGB images captured by a UAV. To evaluate the proposed approach, a self-created dataset of aerial images from different planting blocks is used to train and test the PlantSegNet model...

This study worked with Chunghwa Telecom to collect data from 17 rooftop solar photovoltaic plants installed on top of office buildings, warehouses, and computer rooms in northern, central and southern Taiwan from January 2021 to June 2023. A data pre-processing method combining linear regression and K Nearest Neighbor (k-NN) was proposed to estimate missing values for weather and power generation data. Outliers were processed using historical data and parameters highly correlated with power generation volumes were used to train an artificial intelligence (AI) model...

Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection...

Recent advances in artificial intelligence combined with behavioral sciences have led to the development of cutting-edge tools for recognizing human emotions based on text, video, audio, and physiological data. However, these data sources are expensive, intrusive, and regulated, unlike plants, which have been shown to be sensitive to human steps and sounds. A methodology to use plants as human emotion detectors is proposed. Electrical signals from plants were tracked and labeled based on video data. The labeled data were then used for classification...