Raspberry Pi as Visual Sensor Nodes in Precision Agriculture: A Study

Kamath, Radhika; Balachandra, Mamatha; Prabhu, Srikanth

doi:10.1109/access.2019.2908846

Cited by 69 publications

(45 citation statements)

References 36 publications

(41 reference statements)

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“…The authors named this model as "GEOGLAM" model which ensures the decision support of all the necessary steps for crop security and analysis. Radhika Kamath et al [121], proposed a wireless sensorbased model for monitoring the growth of weeds in paddy crop. The authors performed a study on how Raspberry Pi and WSN can be modeled for precision agriculture.…”

Section: Iot In Crop Monitoringmentioning

confidence: 99%

Recent Developments of the Internet of Things in Agriculture: A Survey

2020

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A rise in the population has immensely increased the pressure on the agriculture sector. With the advent of technology, this decade is witnessing a shift from conventional approaches to the most advanced ones. The Internet of Things (IoT) has transformed both the quality and quantity of the agriculture sector. Hybridization of species along with the real-time monitoring of the farms paved a way for resource optimization. Scientists, research institutions, academicians, and most nations across the globe are moving towards the practice and execution of collaborative projects to explore the horizon of this field for serving mankind. The tech industry is racing to provide more optimal solutions. Inclusion of IoT, along with cloud computing, big data analytics, and wireless sensor networks can provide sufficient scope to predict, process, and analyze the situations and improve the activities in the real-time scenario. The concept of heterogeneity and interoperability of the devices by providing flexible, scalable, and durable methods, models are also opening new domains in this field. Therefore, this paper contributes towards the recent IoT technologies in the agriculture sector, along with the development of hardware and software systems. The public and private sector projects and startup's started all over the globe to provide smart and sustainable solutions in precision agriculture are also discussed. The current scenario, applications, research potential, limitations, and future aspects are briefly discussed. Based on the concepts of IoT a precision farming framework is also proposed in this article.

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Section: Iot In Crop Monitoringmentioning

confidence: 99%

Recent Developments of the Internet of Things in Agriculture: A Survey

2020

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“…For instance, Daskalakis et al [23] presented a novel low-cost and low-power WSN application for leaf sensing using backscatter node/tag which detects water needs of the plants to prevent the water waste of the irrigation systems. Kamath et al [24] designed wireless visual sensor network to monitor paddy crops using Raspberry Pi as visual sensor node and Bluetooth as a communication method. Khan et al [25] presented an energy efficient water utilization and decision support system with the help of WSNs for irrigation of crops.…”

Section: Related Workmentioning

confidence: 99%

A Novel Distributed CDS Algorithm for Extending Lifetime of WSNs With Solar Energy Harvester Nodes for Smart Agriculture Applications

2020

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Recent improvements in computer and software technologies affect different areas of production systems. The productivity of an agricultural process can be accepted as one of the many domains affected by these improvements. The production volume is increased not only with new designs of agricultural machines, but also by utilizing communication technologies in the production process. Even the wireless sensor technologies have started to be used for gathering some environmental features in order to optimize production parameters as well as preserving the product in determined quality levels. However, nodes which play an important part of such systems are prone to battery depletions. To alleviate this battery problem, incorporating harvester nodes into the system has been recently considered. However, only including such nodes is not sufficient for improving the lifetime of the system. In this paper, a new distributed connected dominating set algorithm on WSNs with solar energy harvester nodes for precision agriculture applications is proposed. The novel distributed connected dominating set construction with solar energy harvesting in smart agriculture applications algorithm, namely CDSSEHA, is compared with the traditional flooding methods and with an energy efficient CDS algorithm. According to the results, the proposed algorithm increased the WSNs' lifetime by up to approximately 6 times and 1.4 times compared to the traditional flooding methods and CDS based method, respectively. Furthermore, the CDS construction process constitutes only about 15% of the whole lifetime. INDEX TERMS Distributed connected dominating sets, precision agriculture, smart agriculture applications, solar energy harvester, wireless sensor networks.

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“…And the accuracy of the training set and testing set was calculated respectively to test the effect of classifier and avoid over-fitting. The accuracy calculation method is shown in (8). One hundred test samples were used to verify the classifier performance.…”

Section: F Classifier and Image Preprocessingmentioning

confidence: 99%

“…In recent years, along with advancements in machine vision systems, several approaches using image processing for weed and crop detection have been investigated [8], [9]. There are certain segmentation methods of crops and weeds mainly based on spectral imaging [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

Broccoli Seedling Segmentation Based on Support Vector Machine Combined With Color Texture Features

Zou

Zhang

et al. 2019

IEEE Access

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The segmentation of broccoli seedlings in the crops and weeds co-exist field environment is of great significance for weeding and herbicide spraying. This paper constructed a crop segmentation algorithm with a small training set for discriminating broccoli seedlings from weeds and soil. This algorithm was based on a support vector machine (SVM) combined with color-texture features. Correlation analysis and chi-square tests were used to select 6 features from the 21 color features. Gray-level co-occurrence matrix (GLCM) was used to extract 5 texture features. And each parameter of GLCM had been assessed and optimized by the chi-square test. Linear Discriminant Analysis (LDA) was used to decompose the original dataset in a set of 3 successive orthogonal components. This method selected features more reasonable and gained higher plant segmentation accuracy. When the training sample is greater than 50, the accuracy of the test set could reach 90%. The coefficient of determination (R 2) between the ground truth broccoli seedling area and the segmentation broccoli area was 0.91, and the root-mean-square error (σ) was 0.10. Results demonstrated that the color-texture features were able to effectively segment broccoli seedlings even when there was a significant amount of weeds. INDEX TERMS Pattern recognition, multiple features, support vector machine, broccoli seedling, weed.

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Raspberry Pi as Visual Sensor Nodes in Precision Agriculture: A Study

Cited by 69 publications

References 36 publications

Recent Developments of the Internet of Things in Agriculture: A Survey

Recent Developments of the Internet of Things in Agriculture: A Survey

A Novel Distributed CDS Algorithm for Extending Lifetime of WSNs With Solar Energy Harvester Nodes for Smart Agriculture Applications

Broccoli Seedling Segmentation Based on Support Vector Machine Combined With Color Texture Features

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