1,846 research outputs found

    Agricultural Robot for Intelligent Detection of Pyralidae Insects

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    The Pyralidae insects are one of the main pests in economic crops. However, the manual detection and identification of Pyralidae insects are labor intensive and inefficient, and subjective factors can influence recognition accuracy. To address these shortcomings, an insect monitoring robot and a new method to recognize the Pyralidae insects are presented in this chapter. Firstly, the robot gets images by performing a fixed action and detects whether there are Pyralidae insects in the images. The recognition method obtains the total probability image by using reverse mapping of histogram and multi-template images, and then image contour can be extracted quickly and accurately by using constraint Otsu. Finally, according to the Hu moment characters, perimeter, and area characters, the contours can be filtrated, and recognition results with triangle mark can be obtained. According to the recognition results, the speed of the robot car and mechanical arm can be adjusted adaptively. The theoretical analysis and experimental results show that the proposed scheme has high timeliness and high recognition accuracy in the natural planting scene

    Recognition and Early Stage Detection of <em>Phytophthora</em> in a Crop Farm Using IoT

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    Detection of agricultural plant pests is seen as one of the farmers’ problems. Automated Pest Detection Machine enables early detection of crop insects with advanced computer vision and image recognition. Innovative research in the field of agriculture has demonstrated a new direction by Internet of Things (IoT). IoT needs to be widely experienced at the early stage, so that it is widely used in different farming applications. It allows farmers increase their crop yield with reduced time and greater precision. For the past decade, climate change and precipitation have been unpredictable. Due to this, many Indian farmers are adopting smart methods for environment known as intelligent farming. Smart farming is an automated and IOT-based information technology (Internet of Things). In all wireless environments IOT is developing quickly and widely. The Internet of Things helps to monitor agricultural crops and thus quickly and effectively increase farmers’ income. This paper presents a literature review on IoT devices for recognizing and detecting insects in crop fields. Different types of framework/models are present which are explaining the procedure of insect detection

    Artificial Intelligence System for Automatic Imaging, Quantification, and Identification of Arthropods in Leaf Litter and Pitfall Samples

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    It is well known that arthropods are the most diverse and abundant eukaryotic organisms on the planet. Museum and research collections have huge insect accumulations from expeditions conducted over history that contain specimens of both temporal and spatial value, including hundreds of thousands of species. This biodiversity data is inaccessible to the research community, resulting in a vast amount of “dark data”. The primary objective of this study is to develop an artificial intelligence-driven system for specimen identification that greatly minimizes the time and expertise required to identify specimens in atypical environments. Successful development will have profound impacts on both ecology and biodiversity sciences as it will increase the resolution for ecological studies and allow us to work through the backlog of insect collections, unlocking tremendous amounts of biodiversity data. Development of the system will address multiple challenges in deep learning, including problems associated with limited training data and moving from known domains into unknown. The cutting-edge AI solutions will be a final component in a smart specimen identification system scalable in multiple platforms and across geographic region

    Comparison of CNN Models With Transfer Learning in the Classification of Insect Pests

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    Insect pests are an important problem to overcome in agriculture. The purpose of this research is to classify insect pests with the IP-102 dataset using several CNN pre-trained models and choose which model is best for classifying insect pest data. The method used is the transfer learning method with a fine-tuning approach. Transfer learning was chosen because this technique can use the features and weights that have been obtained during the previous training process. Thus, computation time can be reduced and accuracy can be increased. The models used include Xception, MobileNetV3L, MobileNetV2, DenseNet-201, and InceptionV3. Fine-tuning and freeze layer techniques are also used to improve the quality of the resulting model, making it more accurate and better suited to the problem at hand. This study uses 75,222 image data with 102 classes. The results of this study are the DenseNet-201 model with fine-tuning produces an accuracy value of 70%, MobileNetV2 66%, MobileNetV3L 68%, InceptionV3 67%, Xception 69%. The conclusion of this study is that the transfer learning method with the fine-tuning approach produces the highest accuracy value of 70% in the DenseNet-201 model

    DFF-ResNet : An Insect Pest Recognition Model Based on Residual Networks

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    Insect pest control is considered as a significant factor in the yield of commercial crops. Thus, to avoid economic losses, we need a valid method for insect pest recognition. In this paper, we proposed a feature fusion residual block to perform the insect pest recognition task. Based on the original residual block, we fused the feature from a previous layer between two 1Ă—1 convolution layers in a residual signal branch to improve the capacity of the block. Furthermore, we explored the contribution of each residual group to the model performance. We found that adding the residual blocks of earlier residual groups promotes the model performance significantly, which improves the capacity of generalization of the model. By stacking the feature fusion residual block, we constructed the Deep Feature Fusion Residual Network (DFF-ResNet). To prove the validity and adaptivity of our approach, we constructed it with two common residual networks (Pre-ResNet and Wide Residual Network (WRN)) and validated these models on the Canadian Institute For Advanced Research (CIFAR) and Street View House Number (SVHN) benchmark datasets. The experimental results indicate that our models have a lower test error than those of baseline models. Then, we applied our models to recognize insect pests and obtained validity on the IP102 benchmark dataset. The experimental results show that our models outperform the original ResNet and other state-of-the-art methods

    An Efficient Deep Learning-based approach for Recognizing Agricultural Pests in the Wild

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    One of the biggest challenges that the farmers go through is to fight insect pests during agricultural product yields. The problem can be solved easily and avoid economic losses by taking timely preventive measures. This requires identifying insect pests in an easy and effective manner. Most of the insect species have similarities between them. Without proper help from the agriculturist academician it is very challenging for the farmers to identify the crop pests accurately. To address this issue we have done extensive experiments considering different methods to find out the best method among all. This paper presents a detailed overview of the experiments done on mainly a robust dataset named IP102 including transfer learning with finetuning, attention mechanism and custom architecture. Some example from another dataset D0 is also shown to show robustness of our experimented techniques

    The Research of Disease Spots Extraction Based on Evolutionary Algorithm

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    Image background assessment as a novel technique for insect microhabitat identification

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    The effects of climate change, urbanisation and agriculture are changing the way insects occupy habitats. Some species may utilise anthropogenic microhabitat features for their existence, either because they prefer them to natural features, or because of no choice. Other species are dependent on natural microhabitats. Identifying and analysing these insects' use of natural and anthropogenic microhabitats is important to assess their responses to a changing environment, for improving pollination and managing invasive pests. Traditional studies of insect microhabitat use can now be supplemented by machine learning-based insect image analysis. Typically, research has focused on automatic insect classification, but valuable data in image backgrounds has been ignored. In this research, we analysed the image backgrounds available on the ALA database to determine their microhabitats. We analysed the microhabitats of three insect species common across Australia: Drone flies, European honeybees and European wasps. Image backgrounds were classified as natural or anthropogenic microhabitats using computer vision and machine learning tools benchmarked against a manual classification algorithm. We found flies and honeybees in natural microhabitats, confirming their need for natural havens within cities. Wasps were commonly seen in anthropogenic microhabitats. Results show these insects are well adapted to survive in cities. Management of this invasive pest requires a thoughtful reduction of their access to human-provided resources. The assessment of insect image backgrounds is instructive to document the use of microhabitats by insects. The method offers insight that is increasingly vital for biodiversity management as urbanisation continues to encroach on natural ecosystems and we must consciously provide resources within built environments to maintain insect biodiversity and manage invasive pests.Comment: Submitted in Ecological Informatics journal, first review completed, 19 pages, 10 figure

    Machine learning for detection and prediction of crop diseases and pests: A comprehensive survey

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    Considering the population growth rate of recent years, a doubling of the current worldwide crop productivity is expected to be needed by 2050. Pests and diseases are a major obstacle to achieving this productivity outcome. Therefore, it is very important to develop efficient methods for the automatic detection, identification, and prediction of pests and diseases in agricultural crops. To perform such automation, Machine Learning (ML) techniques can be used to derive knowledge and relationships from the data that is being worked on. This paper presents a literature review on ML techniques used in the agricultural sector, focusing on the tasks of classification, detection, and prediction of diseases and pests, with an emphasis on tomato crops. This survey aims to contribute to the development of smart farming and precision agriculture by promoting the development of techniques that will allow farmers to decrease the use of pesticides and chemicals while preserving and improving their crop quality and production.info:eu-repo/semantics/publishedVersio
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