14,146 research outputs found
Machine Vision Systems â A Tool for Automatic Color Analysis in Agriculture
It was in the early 1960s when machine vision systems initiated researchers and developers have worked on building machines that perform tasks of acquisition, processing, and analysis of images in a wide range of applications for different areas. Currently, along with the new technological advances in electronics, computer systems, image processing, pattern recognition, and mechatronics, it has arose the opportunity to improve machine vision systems development with affordable implementations at lower cost. A machine vision system is the combination of several high-tech techniques, including both hardware and software, used to acquire, process, and analyze images on a machine, which contributes with a set of tools for the extraction of features, such as color and dimension parameters, texture, chemical components, disease detection, freshness, assessment, modeling, and control, among others. Based on former paragraphs, we could say that machine vision systems are appropriate to improve the actual agricultural systems making them more useful, efficient, practical, and reliable
Recognition of Anthracnose Injuries on Apple Surfaces using YOLOV 3-Dense
Plant ailment is one of the essential drivers of harvest yield decrease. With the advancement of PC vision and profound learning innovation, independent discovery of plant surface sore pictures gathered by optical sensors has become a significant research bearing for convenient yield ailment analysis. Right now, anthracnose injury identification strategy dependent on profound learning is proposed. Right off the bat, for the issue of lacking picture information brought about by the irregular event of apple illnesses, notwithstanding conventional picture expansion strategies, Cycle-Consistent Adversarial Network (CycleGAN) profound learning model is utilized right now achieve information increase. These strategies adequately enhance the decent variety of preparing information and give a strong establishment to preparing the identification model. Right now, the premise of picture information increase, thickly associated neural system (DenseNet) is used to streamline highlight layers of the YOLO-V3 model which have lower goals. DenseNet extraordinarily improves the usage of highlights in the neural system and upgrades the identification consequence of the YOLO-V3 model. It is checked in tests that the improved model surpasses Faster R-CNN with VGG16 NET, the first YOLO-V3 model, and other three cutting edge arranges in discovery execution, and it can understand continuous recognition. The proposed technique can be all around applied to the recognition of anthracnose injuries on apple surfaces in-plantations
IoT Resources and Their Practical Application, A Comprehensive Study
The Internet of Things (IoT) has become a paradigm shifter, connecting an enormous number of smart devices and facilitating seamless data exchange for a diverse array of applications. The availability and effective use of the IoT ecosystem's resources are key factors in determining how its practical applications will develop as they mature. The IoT resources and their practical application across several areas are thoroughly explored in this paper. The paper begins by classifying and describing the various sensor types, their applications in various fields, and IoT resources, highlighting their contributions to real-time data collection, processing, and transmission. It then goes on to demonstrate a wide range of real-world uses for these resources, such as smart cities, education, agriculture, business, healthcare, environment monitoring, transportation, and industrial automation. However, utilizing IoT resources effectively is not without difficulties. Critical difficulties such as resource allocation, scalability, security, interoperability, and privacy concerns are identified and discussed in the paper. Furthermore, the paper also highlights future directions and emerging trends in IoT resource management, including edge computing, cloud computing, human machine integration, and compatibility with other systems. These developments aim to increase the dependability of IoT applications in diverse settings and optimize resource allocation. This paper's conclusion highlights the crucial role that IoT resources play in advancing real-world applications across a variety of areas. Researchers, practitioners, policymakers, and other stakeholders may collaborate together to effectively leverage the full potential of IoT resources to build intelligent, effective ecosystems that meet the needs of contemporary society by solving difficulties and utilizing developing trends
Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects
The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production
Semantic Segmentation based deep learning approaches for weed detection
Global increase in herbicide use to control weeds has led to issues such as evolution of herbicide-resistant weeds, off-target herbicide movement, etc. Precision agriculture advocates Site Specific Weed Management (SSWM) application to achieve precise and right amount of herbicide spray and reduce off-target herbicide movement. Recent advancements in Deep Learning (DL) have opened possibilities for adaptive and accurate weed recognitions for field based SSWM applications with traditional and emerging spraying equipment; however, challenges exist in identifying the DL model structure and train the model appropriately for accurate and rapid model applications over varying crop/weed growth stages and environment. In our study, an encoder-decoder based DL architecture was proposed that performs pixel-wise Semantic Segmentation (SS) classifications of crop, soil, and weed patches in the fields. The objective of this study was to develop a robust weed detection algorithm using DL techniques that can accurately and reliably locate weed infestations in low altitude Unmanned Aerial Vehicle (UAV) imagery with acceptable application speed. Two different encoder-decoder based SS models of LinkNet and UNet were developed using transfer learning techniques. We performed various measures such as backpropagation optimization and refining of the dataset used for training to address the class-imbalance problem which is a common issue in developing weed detection models. It was found that LinkNet model with ResNet18 as the encoder section and use of âFocal lossâ loss function was able to achieve the highest mean and class-wise Intersection over Union scores for different class categories while performing predictions on unseen dataset. The developed state-of-art model did not require a large amount of data during training and the techniques used to develop the model in our study provides a propitious opportunity that performs better than the existing SS based weed detections models. The proposed model integrates a futuristic approach to develop a model that could be used for weed detection on aerial imagery from UAV and perform real-time SSWM applications
Advisor: Yeyin Sh
Sensors for product characterization and quality of specialty cropsâA review
This review covers developments in non-invasive techniques for quality analysis and inspection of specialty
crops, mainly fresh fruits and vegetables, over the past decade up to the year 2010. Presented and
discussed in this review are advanced sensing technologies including computer vision, spectroscopy,
X-rays, magnetic resonance, mechanical contact, chemical sensing, wireless sensor networks and radiofrequency
identification sensors. The current status of different sensing systems is described in the
context of commercial application. The review also discusses future research needs and potentials of
these sensing technologies. Emphases are placed on those technologies that have been proven effective
or have shown great potential for agro-food applications. Despite significant progress in the development
of non-invasive techniques for quality assessment of fruits and vegetables, the pace for adoption of these
technologies by the specialty crop industry has been slow
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