Latest Advances in Sensor Applications in Agriculture

Kayad, Ahmed; Paraforos, Dimitrios S.; Marinello, Francesco; Fountas, Spyros

doi:10.3390/agriculture10080362

Cited by 33 publications

(14 citation statements)

References 27 publications

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“…Proximal and Field Sensors [5][6][7] Medium Remote Sensors [8][9][10] Embedded Electronics, Telemetry, and Automation [12,37] Deep Learning and Internet of Things [17][18][19][20] High Cloud Computing, Big Data, Blockchain and Cryptography [3,15,16] Artificial Intelligence [23] The questionnaire was prepared, organized, and made available to the public through the online platform LimeSurvey under the registration number 25889/2020 [38]. The system was available from 17 April to 2 June 2020, allowing answers to be collected based on specific questions previously defined ( Table 2).…”

Section: Reference Complexity Of Applicationsmentioning

confidence: 99%

Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers

et al. 2020

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The rapid population growth has driven the demand for more food, fiber, energy, and water, which is associated to an increase in the need to use natural resources in a more sustainable way. The use of precision agriculture machinery and equipment since the 1990s has provided important productive gains and maximized the use of agricultural inputs. The growing connectivity in the rural environment, in addition to its greater integration with data from sensor systems, remote sensors, equipment, and smartphones have paved the way for new concepts from the so-called Agriculture 4.0 or Digital Agriculture. This article presents the results of a survey carried out with 504 Brazilian farmers about the digital technologies in use, as well as current and future applications, perceived benefits, and challenges. The questionnaire was prepared, organized, and made available to the public through the online platform LimeSurvey and was available from 17 April to 2 June 2020. The primary data obtained for each question previously defined were consolidated and analyzed statistically. The results indicate that 84% of the interviewed farmers use at least one digital technology in their production system that differs according to technological complexity level. The main perceived benefit refers to the perception of increased productivity and the main challenges are the acquisition costs of machines, equipment, software, and connectivity. It is also noteworthy that 95% of farmers would like to learn more about new technologies to strengthen the agricultural development in their properties.

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Section: Reference Complexity Of Applicationsmentioning

confidence: 99%

Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers

et al. 2020

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“…For example, the Microsoft Kinect sensor developed in the last decade shows remarkable potential for rapid characterization of vegetation structure, as reported previously [91]. For a comprehensive study on the latest advances in sensor technology applied to the agricultural sector, the study by Kayad et al is recommended [26].…”

Section: Synthetic Aperture Radar (Sar)mentioning

confidence: 95%

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

et al. 2021

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Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.

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“…The growth of small data collecting units has been rapid in recent years as they can cover data details for broad coverage, enhancing understanding of crop spatial and temporal variability [40]. Sensors with smaller dimensions, lower costs, and higher performance that can be deployed and integrated into production processes allow for more data and, ultimately, more information [41]. In the experiment conducted in [42], the Smart Engine (OtO Photonics, Inc., Hsinchu City, Taiwan) (SE) Series Spectrometer was used to collect the reflectance of FFB, with the scanning process focused only on the front equatorial of the FFB, while the classification made use of the pure reflectance of the FFB (180 to 1100 nm) as the input.…”

Section: Wetmentioning

confidence: 99%

Application of Optical Spectrometer to Determine Maturity Level of Oil Palm Fresh Fruit Bunches Based on Analysis of the Front Equatorial, Front Basil, Back Equatorial, Back Basil and Apical Parts of the Oil Palm Bunches

2021

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The quality of palm oil depends on the maturity level of the oil palm fresh fruit bunch (FFB). This research applied an optical spectrometer to collect the reflectance data of 96 FFB from unripe, ripe, and overripe classes for the maturity level classification. The spectrometer scanned the FFB from different parts, including apical, front equatorial, front basil, back equatorial, and back basil. Principal component analysis was carried out to extract principal components from the reflectance data of each of the parts. The extracted principal components were used in an ANOVA test, which found that the reflectance data of the front equatorial showed statistically significant differences between the three maturity groups. Then, the collected reflectance data was subjected to machine learning training and testing by using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The front equatorial achieved the highest accuracy, of 90.6%, by using SVM as classifiers; thus, it was proven to be the most optimal part of FFB that can be utilized for maturity classification. Next, the front equatorial dataset was divided into UV (180–400 nm), blue (450–490 nm), green (500–570 nm), red (630–700 nm), and NIR (800–1100 nm) regions for classification testing. The UV bands showed a 91.7% accuracy. After this, representative bands of 365, 460, 523, 590, 623, 660, 735, and 850 nm were extracted from the front equatorial dataset for further classification testing. The 660 nm band achieved an 89.6% accuracy using KNN as a classifier. Composite models were built from the representative bands. The combination of 365, 460, 735, and 850 nm had the highest accuracy in this research, which was 93.8% with the use of SVM. In conclusion, these research findings showed that the front equatorial has the better ability for maturity classification, whereas the composite model with only four bands has the best accuracy. These findings are useful to the industry for future oil palm FFB classification research.

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Latest Advances in Sensor Applications in Agriculture

Cited by 33 publications

References 27 publications

Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers

Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

Application of Optical Spectrometer to Determine Maturity Level of Oil Palm Fresh Fruit Bunches Based on Analysis of the Front Equatorial, Front Basil, Back Equatorial, Back Basil and Apical Parts of the Oil Palm Bunches

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