A survey on the 5G network and its impact on agriculture: Challenges and opportunities

Tang, Yu; Dananjayan, Sathian; Hou, Chao; Guo, Qiwei; Luo, Shaoming; He, Yong

doi:10.1016/j.compag.2020.105895

Cited by 227 publications

(108 citation statements)

References 117 publications

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“…It is promising that smart farming and agriculture employs several types of sensors, such as multispectral imaging, thermal infrared imaging, hyperspectral, and lidar, to increase the quantity and quality of agricultural products with minimal loss and labor [ 36 ]. MSI is an emerging, fast, and non-destructive identification method for aged seeds, while it is difficult to distinguish aged seeds and non-aged seeds by visual inspection or traditional experiments.…”

Section: Discussionmentioning

confidence: 99%

Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis

Wang

Zhang

Song

et al. 2021

Sensors

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Seed aging detection and viable seed prediction are of great significance in alfalfa seed production, but traditional methods are disposable and destructive. Therefore, the establishment of a rapid and non-destructive seed screening method is necessary in seed industry and research. In this study, we used multispectral imaging technology to collect morphological features and spectral traits of aging alfalfa seeds with different storage years. Then, we employed five multivariate analysis methods, i.e., principal component analysis (PCA), linear discrimination analysis (LDA), support vector machines (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) to predict aged and viable seeds. The results revealed that the mean light reflectance was significantly different at 450~690 nm between non-aged and aged seeds. LDA model held high accuracy (99.8~100.0%) in distinguishing aged seeds from non-aged seeds, higher than those of SVM (87.4~99.3%) and RF (84.6~99.3%). Furthermore, dead seeds could be distinguished from the aged seeds, with accuracies of 69.7%, 72.0% and 97.6% in RF, SVM and LDA, respectively. The accuracy of nCDA in predicting the germination of aged seeds ranged from 75.0% to 100.0%. In summary, we described a nondestructive, rapid and high-throughput approach to screen aged seeds with various viabilities in alfalfa.

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Section: Discussionmentioning

confidence: 99%

Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis

Wang

Zhang

Song

et al. 2021

Sensors

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“…We have achieved a maximum of 12 km in mountain environment with enough link budget. Systems based in communications provided by TSPs (GPRS [6], satellite [44], NBioT [45], SigFox [7]) do not require deployment of any additional communication infrastructure, but they will not work where there is no coverage. Furthermore, cellular links use much more energy for communication than LoRa; 2 W [46] against 25 mW.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure

et al. 2021

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Extensive unsupervised livestock farming is a habitual technique in many places around the globe. Animal release can be done for months, in large areas and with different species packing and behaving very differently. Nevertheless, the farmer’s needs are similar: where livestock is (and where has been) and how healthy they are. The geographical areas involved usually have difficult access with harsh orography and lack of communications infrastructure. This paper presents the design of a solution for extensive livestock monitoring in these areas. Our proposal is based in a wearable equipped with inertial sensors, global positioning system and wireless communications; and a Low-Power Wide Area Network infrastructure that can run with and without internet connection. Using adaptive analysis and data compression, we provide real-time monitoring and logging of cattle’s position and activities. Hardware and firmware design achieve very low energy consumption allowing months of battery life. We have thoroughly tested the devices in different laboratory setups and evaluated the system performance in real scenarios in the mountains and in the forest.

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“…With the further development of hardware and communication technologies, the Internet of Things (IoT) technology has emerged as a continuation of WSN. IoT is also applied in precision agriculture [ 3 ]. IoT enables the collection of sensor data and their transmission to the Internet, where it is processed using machine learning and can provide adequate information relevant to crop management [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Pest Insect Appearance Using Sensors and Machine Learning

Marković

Vujicic

Tanasković

et al. 2021

Sensors

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The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect’s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields.

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A survey on the 5G network and its impact on agriculture: Challenges and opportunities

Cited by 227 publications

References 117 publications

Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis

Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis

Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure

Prediction of Pest Insect Appearance Using Sensors and Machine Learning

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