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Edge Devices In Agriculture For Real-time Crop Monitoring And Analysis
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Is Cloud Computing In Agriculture Moving Near The Edge?
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By Mateus Cruz Mateus Cruz Scilit Preprints.org Google Scholar, Samuel Mafra Samuel Mafra Scilit Preprints.org Google Scholar *, Eduardo Teixeira Eduardo Teixeira Scilit Preprints.org Google Scholar and Felipe Figueiredo Felipe Figueiredo Scilit.
Ai Delivers Real Time Data For Smarter Farming
Entry: 15 June 2022 / Revised: 27 July 2022 / Accepted: 29 July 2022 / Published: 5 August 2022
Strawberries are sensitive fruits that are susceptible to various pests and diseases. Therefore, agrochemicals and pesticides are intensively used during production. Due to their sensitivity to extreme temperature or humidity, it can cause various damage to planting and fruit quality. To reduce this problem, this study developed an innovative technology that is able to collect, analyze, predict and detect heterogeneous data in strawberry cultivation. The proposed IoT platform integrates various monitoring services into a common platform for the digital economy. The system connects and manages Internet of Things (IoT) devices to analyze environmental and crop information. In addition, a computer vision model using the Yolo v5 architecture detects seven common strawberry diseases in real time. This model supports effective disease diagnosis with 92% accuracy. In addition, the system supports LoRa communication for data transmission between nodes over long distances. In addition, the IoT platform integrates machine learning capabilities to find outliers in the collected data and provides reliable information for the user. All these technologies are combined to reduce disease problems and environmental damage in crops. The proposed system will be validated through implementation and tested on a watermelon farm where the capabilities will be analyzed and evaluated.
Strawberries are a popular fruit eaten almost all over the world, and this spread is the result of the fruit’s adaptability, which makes it possible to grow in different climates. The efforts of producers and scientists have also contributed to increasing the production and commercialization of strawberries, creating adaptive systems that allow cultivation in the specific conditions of each region . In addition, there is production and trade of watermelon in 76 countries of the world, the world production of which reached 7.7 million tons in 2013. Its consumption has increased in the 21st century, along with technological innovations that allow strawberries to be available year-round. Thus, meeting the constant market demand .
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One of the challenges faced is to produce the fruit economically and sustainably , as traditional cultivation methods use large amounts of pesticides and agrochemicals to prevent damage from diseases and pests. About half of the world’s crops are lost due to the attack of insects and diseases . For example, anthracnose (a disease caused by Colletorihum spp) is the most destructive disease in Brazilian watermelon farms . As a result, farmers use two common methods to solve this problem: prevention and control. Prevention is associated with good practices such as (i) obtaining plants from reliable suppliers, (ii) water quality control, (iii) sanitation and maintenance of work tools and (iv) personal equipment. Reference . At the same time, it is important to control disease or plague when the disease reaches a critical stage, which occurs when prevention is insufficient or not used properly. Most warfare practices use chemical products to eradicate disease . However, this product can cause health problems for consumers and farmers. Furthermore, these diseases can lead to serious health problems such as hearing loss and birth defects through changes in the maternal-fetal binomial .
Accurate identification of diseases is important to use the right products and thus reduce or eliminate disease or pests in the plantation, which sometimes requires prior knowledge of the specialist or the farmer. In addition, some identification processes require taking samples and sending them to a laboratory for analysis, which is time-consuming, leaving the farm vulnerable and allowing the disease to spread elsewhere . On the other hand, researchers are starting to investigate new approaches based on artificial intelligence and mobile devices, trying to develop accurate and reliable methods of disease diagnosis. In addition, image-based systems and methods using artificial intelligence are applied to plant leaves, fruits, and stems due to significant visual changes caused by most diseases.
Computer vision (CV) is a field of artificial intelligence that aims to provide vision capabilities to machines. The field of computer vision has provided solutions and applications related to agriculture that offer autonomous and efficient methods of growing different plants . Disease control has been widely studied by researchers, and several applications can be found in the literature that use computer vision to detect pests and diseases . Fruit quality control systems are also gaining ground in the field of AI, leading to the development of several programs for automatic quality control of cut fruits . Most of the proposed agricultural applications in the literature that use vision systems often use neural networks for process optimization. Neural network based systems can detect and classify diseases in real time after proper training process. In fact, various diseases can be quickly identified and the farmer can take appropriate measures to reduce or eliminate the problem. Several researchers have already developed and proposed solutions that use neural networks to detect various diseases in different plants, such as corn , tomato  and many others .
Iot In Agriculture And Smart Farming
This paper presents a unified IoT platform with wireless sensor network (WSN), computer vision (CV), machine learning (ML), and long-range communication (LoRa) capabilities. In addition, the paper presents a cost-effective and flexible way to implement the proposed IoT platform for heterogeneous scenarios and agricultural processes. In addition, the platform makes all captured metrics available for manual analysis and data-driven decisions. The collected data is stored in the cloud and on the Raspberry Board, resulting in redundancy in all data and offering different approaches to farmers. In short, the goal of the project is to create a comprehensive IoT platform to enable intelligent farming in strawberry cultivation. The organization of the article is as follows. Section 2 presents some of the background technologies used in building the platform. Section 3 provides a review of related literature. Section 4 provides a detailed overview of the proposed IoT platform. Finally, chapter 5 presents the results obtained during the test.
Wireless Sensor Networks (WSN) is defined as a wireless network of sensors connected to devices such as microcontrollers to monitor variables or phenomena, allowing users to monitor various and heterogeneous subjects in real time. In addition, wireless data exchange between devices also allows for a wide range of possibilities, as traditional methods that use wires to exchange data are not applicable in solutions that require long distances between devices. Furthermore, WSNs can address multiple and unpredictable ecosystems in agriculture by monitoring and measuring various physical aspects and phenomena. Similarly, the amount of data collected through remote sensing can provide a broad view of the agricultural environment and has several advantages through a non-invasive method of collecting information over a large geographic area.
Most sensor network applications aim to develop an independent and remote monitoring system for variables such as humidity, temperature, and soil moisture [13, 14], and most of the developed systems monitor only on the screen. ] . Some researchers have made progress in this field, using IoT devices to achieve significant distances , automation of farm irrigation systems , protection of silage from rodents , etc. In addition, low-cost wireless sensor networks can be vulnerable to external attacks and inconsistent data. Several researchers have developed existing ways to mitigate the risk of network attacks and the integrity and quality of collected data . The proposed platform uses the WSN method to collect plantation data. In addition, the platform uses
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