Detection Of Plant Pests Research Articles

An improved YOLOv5-based apple leaf disease detection method

The effective identification of fruit tree leaf disease is of great practical significance to reduce pesticide spraying, improve fruit yield and realize ecological agriculture. Computer vision technology can be effectively identifying and prevent plant diseases and insect pests. However, the lack of consideration of disease diversity and accuracy of existing detection models hinders their application and development in the field of plant pest detection. This paper proposes an efficient detection model of apple leaf disease spot through the improvement of the traditional Yolov5 detection network called A-Net. In order to significantly increase the A-Net's detection speed and accuracy, the A-Net model applies the loss function Wise-IoU, which includes the attention mechanism and the dynamic focusing mechanism, to the Yolov5 network model. The RepVGG module is then used to replace the original model's convolution module. The experimental results show that the improved model effectively suppresses the growth of some error weights. Compared with several object detection models, the improved A-Net model has a Mean Average Precision across IoU threshold 0.5 and an accuracy of 92.7%, which fully proves that the improved A-Net model has more advantages in detecting apple leaf diseases.

IoT Based Smart Plant Monitoring System

The agricultural sector is in transition thanks to the Internet of Things (IoT), which is also helping farmers overcome the many obstacles they confront. The problems that IoT can help with are plant and soil monitoring, livestock monitoring, and conservation monitoring. Innovative IoT applications improve agricultural productivity in terms of quality, quantity, sustainability and cost-effectiveness while meeting industry challenges. This project automates the watering process while monitoring soil factors such as temperature, electrical conductivity, and moisture content. A microcontroller is used to make decisions. When a user is acknowledged in the field whether there is a text message variation from the anticipated values. This research also includes plant pest detection in addition to soil metrics. This guarantees whole system health. In contrast, greenhouses are regulated environments where plants are grown. The primary goal of this project is to create an Arduino- based system that is easy to use, inexpensive, and can be used to continuously upload and alter environmental parameter values in order to maximize plant growth and yield.

Early Detection of Fusarium Basal Rot Infection in Onions and Shallots Based on VOC Profiles Analysis.

Gas chromatography ion-mobility spectrometry (GC-IMS) technology is drawing increasing attention due to its high sensitivity, low drift, and capability for the identification of compounds. The noninvasive detection of plant pests and pathogens is an application area well suited to this technology. In this work, we employed GC-IMS technology for early detection of Fusarium basal rot in brown onion, red onion, and shallot bulbs and for tracking disease progression during storage. The volatile profiles of the infected and healthy control bulbs were characterized using GC-IMS and gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS). GC-IMS data combined with principal component analysis and supervised methods provided discrimination between infected and healthy control bulbs as early as 1 day after incubation with the pathogen, classification regarding the proportion of infected to healthy bulbs in a sample, and prediction of the infection's duration with an average R2 = 0.92. Furthermore, GC-TOF-MS revealed several compounds, mostly sulfides and disulfides, that could be uniquely related to Fusarium basal rot infection.

Technical report on the classification of plant pest detection and identification methods

Technical report on the classification of plant pest detection and identification methods

Roselle Pest Detection and Classification Using Threshold and Template Matching

Roselle is a fiber-producing plant that has broad benefits for health food, so many farmers are interested in starting to cultivate it. This study aims to design a rosella plant pest detection system to reduce the risk of crop failure or reduced yields of rosella calyx. The design of a system for detecting and classifying rosella pests uses the threshold method as a digital image processing method connected via the internet with information media applications and template matching to detect and classify pests on rosella plants. Detection of pests on rosella plants has been successfully built using a detection system using thresholding and template matching methods. Datasets of rosella plant pests that are not yet widely available encourage the detection of rosella plant pests with datasets from rosella plant objects and limited data testing. Testing with 75% accuracy, the detection process is affected by light and camera quality.

Survey on crop pest detection using deep learning and machine learning approaches.

The most important elements in the realm of commercial food standards are effective pest management and control. Crop pests can make a huge impact on crop quality and productivity. It is critical to seek and develop new tools to diagnose the pest disease before it caused major crop loss. Crop abnormalities, pests, or dietetic deficiencies have usually been diagnosed by human experts. Anyhow, this was both costly and time-consuming. To resolve these issues, some approaches for crop pest detection have to be focused on. A clear overview of recent research in the area of crop pests and pathogens identification using techniques in Machine Learning Techniques like Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT), Naive Bayes (NB), and also some Deep Learning methods like Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Deep convolutional neural network (DCNN), Deep Belief Network (DBN) was presented. The outlined strategy increases crop productivity while providing the highest level of crop protection. By offering the greatest amount of crop protection, the described strategy improves crop efficiency. This survey provides knowledge of some modern approaches for keeping an eye on agricultural fields for pest detection and contains a definition of plant pest detection to identify and categorise citrus plant pests, rice, and cotton as well as numerous ways of detecting them. These methods enable automatic monitoring of vast domains, therefore lowering human error and effort.

Prediction of plant pest detection using improved mask FRCNN in cloud environment

According to the Indian Food and Agriculture Organizations (FAO), agricultural plants can disrupt the prospect of a worldwide productivity loss of about 20–40%. To avoid these overwhelming insect plants and increase crop yield, smart farming for farmers was developed using modern technology with Artificial Intelligence (AI).The foremost objective of this paper is to apprehend the advanced application for operational agricultural plantonline recognition on plant open fields such as greenhouses and large farms. This paper proposes Deep Learning (DL) for experts and agriculturalists with a help of an innovative mobile application to control the insects which affect plants. Improved MaskFaster Region-Based Convolutional Neural Network (IMFR-CNN) method proposed for developing technologically advanced application exploits to undertake the recognize exhaust of insect plants in Cloud Computing (CC). Additionally, for mentoring the farmers, the planticides database was maintained to suggest theplanticides andprovide the details of the spotted crop plants to the farmers. Four groups of plantsGram pod borer, Tobacco caterpillar, Whitefly, Spider mites, and Root-knot nematode are considered in this research.The proposed IMFRCNN model evaluated and produces the maximum accuracy of 99.0% for every confirmed plant image for detecting the plant disease at an earlier stage than the existing methods such as Back Propagation (BP) and Single Shot Multi-Box Detector (SSD) DL method.

Technology development for the early detection of plant pests: a framework for assessing Technology Readiness Levels (TRLs) in environmental science

Innovation in environmental fields such as plant health is complex because of unbounded challenges and lack of certainty of commercial uptake. In this paper we present a Technology Readiness Level (TRL) framework, specifically to assist with assessment of technologies to support detection of tree pests and pathogens, but also for wider potential adaptation. Biosecurity can be enhanced by improved early detection of pests and pathogens, but development and deployment of new technologies requires robust scrutiny. We critically analyse the concept, practice and applicability of TRLs. Interviews revealed scientist perspectives during the development process of five novel early plant pest and pathogen detection technologies. A retrospective, collective narrative of one technology from concept to commercial deployment was undertaken. We then developed a calculator tool for assessment of biosecurity TRLs. Our findings illustrate the iterative process of technology development, the challenges in final TRLs of acquiring funding to move from proven success to viable product, inefficiencies created through the need for multiple projects for each technology and the imperative to consider the wider socio-ecological technical landscape, including policy context. End user engagement was particularly valuable at beginning and end of the TRL scale. We conclude that the TRL framework comprises a robust approach to assess technologies in that it facilitates progress tracking, evaluation of success likelihood and identification of opportunities for investment. However, its potential will only be realised for environmental management if it is integrated into the socio-ecological technical landscape and wider discussions regarding knowledge co-production and valuing nature.

Emerging Methods of Monitoring Volatile Organic Compounds for Detection of Plant Pests and Disease.

Each year, unwanted plant pests and diseases, such as Hendel or potato soft rot, cause damage to crops and ecosystems all over the world. To continue to feed the growing population and protect the global ecosystems, the surveillance and management of the spread of these pests and diseases are crucial. Traditional methods of detection are often expensive, bulky and require expertise and training. Therefore, inexpensive, portable, and user-friendly methods are required. These include the use of different gas-sensing technologies to exploit volatile organic compounds released by plants under stress. These methods often meet these requirements, although they come with their own set of advantages and disadvantages, including the sheer number of variables that affect the profile of volatile organic compounds released, such as sensitivity to environmental factors and availability of soil nutrients or water, and sensor drift. Furthermore, most of these methods lack research on their use under field conditions. More research is needed to overcome these disadvantages and further understand the feasibility of the use of these methods under field conditions. This paper focuses on applications of different gas-sensing technologies from over the past decade to detect plant pests and diseases more efficiently.

Can general surveillance detect high priority pests in the Western Australian Grains Industry?

Initial detection of plant pests (includes diseases) is often made by the growers and consultants that work directly with crops; this form of detection is referred to as general surveillance. General surveillance activities have an important role in plant biosecurity for the early detection of pests and for demonstrating area freedom. In the Western Australian grains industry, reports of suspect high priority pests to the state Department of Agriculture and Food is the main form of general surveillance. There is little information on the level of knowledge of high priority pests in the grains industry in Western Australia and the ability of members to detect high priority pests if they were present. This study details a survey of members of the grains industry to determine their knowledge of the signs of high priority grains pests and the likelihood that they would detect a high priority pest if it were present in grain or crops. Knowledge of the signs of the high priority grain diseases, Karnal bunt and Barley stripe rust, were found to be higher than those of the high priority insect pests, Khapra beetle and Russian wheat aphid. A wide range of responses was collected from respondents’ self-rated likelihood of detecting each of these pests and diseases in crop or harvested grain. However, respondents were more confident in their ability to detect the two plant diseases than the two insect pests. The results of this study suggest that further training in recognition of the signs and symptoms of high priority grains pests is required to increase the ability of members of the grains industry to confidently detect and differentiate high priority pests from common pests in the course of routine activities.

Evaluation of High‐resolution Melting Curve Analysis as a New Tool for Root‐knot Nematode Diagnostics

AbstractIn the detection of plant pests, speed and accuracy are vital. High‐resolution melting curve (HRMC) analysis was therefore evaluated as a new tool for the identification of root‐knot nematodes (Meloidogyne spp.). On the basis of the second intergenic spacer (IGS2) region of the ribosomal DNA cistron, Meloidogyne chitwoodi, M. fallax and M. hapla were successfully distinguished from each other and the group of the three tropical species, M. incognita, M. arenaria and M. javanica. Conversely, it was shown that the IGS2 region is not suitable for the tropical species M. enterolobii (senior synonym of M. mayaguensis) as the amplification of multiple fragments of different lengths prevented a reliable HRMC analysis. However, the obtained results provide a proof of principle that HRMC analysis can be a suitable single‐tube assay for fast and accurate root‐knot nematode identification.

Risk maps for targeting exotic plant pest detection programs in the United States

In the United States, pest risk maps are used by the Cooperative Agricultural Pest Survey for spatial and temporal targeting of exotic plant pest detection programs. Methods are described to create standardized host distribution, climate and pathway risk maps for the top nationally ranked exotic pest targets. Two examples are provided to illustrate the risk mapping process: late wilt of corn (Harpophora maydis) and the giant African land snail (Achatina fulica). Host risk maps were made from county‐level crop census and USDA Forest Inventory and Analysis data, respectively. Climate risk maps were made using the North Carolina State University–USDA APHIS Plant Pest Forecasting System (NAPPFAST), which uses a web‐based graphical user interface to link climatic and geographic databases with interactive templates for biological modelling. Pathway risk maps were made using freight flow allocation data sets to move commodities from 7 world regions to 3162 US urban areas. A new aggregation technique based on the Pareto dominance principle was used to integrate maps of host abundance, climate and pathway risks into a single decision support product. The maps are publicly available online (http://www.nappfast.org). Key recommendations to improve the risk maps and their delivery systems are discussed.

Integrating drivers influencing the detection of plant pests carried in the international cut flower trade

This paper analyses the cut flower market as an example of an invasion pathway along which species of non-indigenous plant pests can travel to reach new areas. The paper examines the probability of pest detection by assessing information on pest detection and detection effort associated with the import of cut flowers. We test the link between the probability of plant pest arrivals, as a precursor to potential invasion, and volume of traded flowers using count data regression models. The analysis is applied to the UK import of specific genera of cut flowers from Kenya between 1996 and 2004. There is a link between pest detection and the Genus of cut flower imported. Hence, pest detection efforts should focus on identifying and targeting those imported plants with a high risk of carrying pest species. For most of the plants studied, efforts allocated to inspection have a significant influence on the probability of pest detection. However, by better targeting inspection efforts, it is shown that plant inspection effort could be reduced without increasing the risk of pest entry. Similarly, for most of the plants analysed, an increase in volume traded will not necessarily lead to an increase in the number of pests entering the UK. For some species, such as Carthamus and Veronica, the volume of flowers traded has a significant and positive impact on the likelihood of pest detection. We conclude that analysis at the rank of plant Genus is important both to understand the effectiveness of plant pest detection efforts and consequently to manage the risk of introduction of non-indigenous species.