Towards real-time crops surveillance for disease classification: exploiting parallelism in computer vision

Akram, Tallha; Naqvi, Syed Rameez; Haider, Sajjad; Kamran, Muhammad

doi:10.1016/j.compeleceng.2017.02.020

Cited by 41 publications

(21 citation statements)

References 6 publications

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“…Considering the fact that domestic consumption of wheat continues to increase steadily, its exports are expected to be affected significantly in the coming years, which will be a major setback for the country's economy. Wheat production forecasting for many years to come is one way of alleviating this predicament; an analysis of its production to consumption ratio will not just be means of estimating the expected amount of export, but will motivate certain measures to be taken at state level to ensure its escalated cultivation, and to minimize losses due to diseases by modern technological methods [2].…”

Section: Introductionmentioning

confidence: 99%

LSTM Neural Network Based Forecasting Model for Wheat Production in Pakistan

et al. 2019

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Pakistan’s economy is largely driven by agriculture, and wheat, mostly, stands out as its second most produced crop every year. On the other hand, the average consumption of wheat is steadily increasing as well, due to which its exports are not proportionally growing, thereby, threatening the country’s economy in the years to come. This work focuses on developing an accurate wheat production forecasting model using the Long Short Term Memory (LSTM) neural networks, which are considered to be highly accurate for time series prediction. A data pre-processing smoothing mechanism, in conjunction with the LSTM based model, is used to further improve the prediction accuracy. A comparison of the proposed mechanism with a few existing models in literature is also given. The results verify that the proposed model achieves better performance in terms of forecasting, and reveal that while the wheat production will gradually increase in the next ten years, the production to consumption ratio will continue to fall and pose threats to the overall economy. Our proposed framework, therefore, may be used as guidelines for wheat production in particular, and is amenable to other crops as well, leading to sustainable agriculture development in general.

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Section: Introductionmentioning

confidence: 99%

LSTM Neural Network Based Forecasting Model for Wheat Production in Pakistan

et al. 2019

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“…LAI is used in precision agriculture to control the application rate in sensor-based fungicide spraying in cereal crops in different locations within a given field [21]. The difference is that GNDVI uses the green instead of the red visible channel, as shown in (4).…”

Section: Ndv Imentioning

confidence: 99%

“…A total of 120 measurements were performed at the same points for the comparison, and the obtained results can be seen in the charts in Figure 12, in which Figure fig:samplesOfNDVIa presents the raw measurements and Figure fig:samplesOfNDVIb presents the difference between the values measured by the GNDVI and the GreenSeeker. Whereas the GreenSeeker uses the red channel to calculate NDVI (3), the developed algorithm uses the green channel, as shown in (4), as this hardware resource is not available in Raspberry Pi NoIR camera. Therefore, a small variation in the achieved steady levels may be expected due to the different bands used, which was noticed as can be seen in Figure 12b.…”

Section: Field Experimentsmentioning

confidence: 99%

“…Through images captured by RGB or infrared cameras carried by a UAV, image processing algorithms can access the spatial domain of the pixel matrix which composes the image where the UAV is inserted. Consequently, it is possible to identify patterns or common elements on the images in real time [3][4][5]. These patterns allow for the extracting of data that can be used as the input in embedded autonomous decisions and control systems.…”

Section: Introductionmentioning

confidence: 99%

“…Abstracting techniques from [15,16] allowed the mathematical optimization of the proposed image processing algorithm. Following [7,10], it was possible to abstract techniques for the use of an infrared camera, and from [4,10], techniques for image processing with illumination variations present in images used in precision agriculture applications.…”

Section: Introductionmentioning

confidence: 99%

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Proposal for an Embedded System Architecture Using a GNDVI Algorithm to Support UAV-Based Agrochemical Spraying

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An important area in precision agriculture is related to the efficient use of chemicals applied onto fields. Efforts have been made to diminish their use, aiming at cost reduction and fewer chemical residues in the final agricultural products. The use of unmanned aerial vehicles (UAVs) presents itself as an attractive and cheap alternative for spraying pesticides and fertilizers compared to conventional mass spraying performed by ordinary manned aircraft. Besides being cheaper than manned aircraft, small UAVs are capable of performing fine-grained instead of the mass spraying. Observing this improved method, this paper reports the design of an embedded real-time UAV spraying control system supported by onboard image processing. The proposal uses a normalized difference vegetation index (NDVI) algorithm to detect the exact locations in which the chemicals are needed. Using this information, the automated spraying control system performs punctual applications while the UAV navigates over the crops. The system architecture is designed to run on low-cost hardware, which demands an efficient NDVI algorithm. The experiments were conducted using Raspberry Pi 3 as the embedded hardware. First, experiments in a laboratory were conducted in which the algorithm was proved to be correct and efficient. Then, field tests in real conditions were conducted for validation purposes. These validation tests were performed in an agronomic research station with the Raspberry hardware integrated into a UAV flying over a field of crops. The average CPU usage was about 20% while memory consumption was about 70 MB for high definition images, with 4% CPU usage and 20.3 MB RAM being observed for low-resolution images. The average current measured to execute the proposed algorithm was 0.11 A. The obtained results prove that the proposed solution is efficient in terms of processing and energy consumption when used in embedded hardware and provides measurements which are coherent with the commercial GreenSeeker equipment.

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Towards real-time crops surveillance for disease classification: exploiting parallelism in computer vision

Cited by 41 publications

References 6 publications

LSTM Neural Network Based Forecasting Model for Wheat Production in Pakistan

LSTM Neural Network Based Forecasting Model for Wheat Production in Pakistan

Proposal for an Embedded System Architecture Using a GNDVI Algorithm to Support UAV-Based Agrochemical Spraying

Introduction to Asynchronous Circuit Design

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