Low-cost multispectral imaging system for crop monitoring

de, Andres Montes; Arreola, Luis; Flores, Alejandro; Jorge, J.; Flores, Gerardo

doi:10.1109/icuas.2018.8453426

Cited by 42 publications

(20 citation statements)

References 18 publications

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“…Therefore, the RGBN camera records visible and NIR light in a specified channel(s) depending on the filter applied. Most previous studies calculated NDVI still with the formula (NIR -Red)/(NIR + Red) by using the corresponding channel values obtained from RGBN camera as Red and NIR, respectively [1,16,[22][23][24][25][26][27]. However, these results were somehow different from the NDVI by HSC or MSC.…”

Section: Introductionmentioning

confidence: 94%

Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants

Wang

Duan

Zhang

et al. 2020

Sensors

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The normalized difference vegetation index (NDVI) is widely used in remote sensing to monitor plant growth and chlorophyll levels. Usually, a multispectral camera (MSC) or hyperspectral camera (HSC) is required to obtain the near-infrared (NIR) and red bands for calculating NDVI. However, these cameras are expensive, heavy, difficult to geo-reference, and require professional training in imaging and data processing. On the other hand, the RGBN camera (NIR sensitive RGB camera, simply modified from standard RGB cameras by removing the NIR rejection filter) have also been explored to measure NDVI, but the results did not exactly match the NDVI from the MSC or HSC solutions. This study demonstrates an improved NDVI estimation method with an RGBN camera-based imaging system (Ncam) and machine learning algorithms. The Ncam consisted of an RGBN camera, a filter, and a microcontroller with a total cost of only $70 ~ 85. This new NDVI estimation solution was compared with a high-end hyperspectral camera in an experiment with corn plants under different nitrogen and water treatments. The results showed that the Ncam with two-band-pass filter achieved high performance (R2 = 0.96, RMSE = 0.0079) at estimating NDVI with the machine learning model. Additional tests showed that besides NDVI, this low-cost Ncam was also capable of predicting corn plant nitrogen contents precisely. Thus, Ncam is a potential option for MSC and HSC in plant phenotyping projects.

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Section: Introductionmentioning

confidence: 94%

Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants

Wang

Duan

Zhang

et al. 2020

Sensors

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show abstract

“…Also, an MIS operating in the spectral range of 405-970 nm consisting of 19 different spectral bands has been used to determine the aerobic plate count of cooked pork sausages [58]. In addition, SI has been used for crop monitoring, and vegetation index calculation [59] and to assess the health status of crops [60]. In [61], an MIS has been developed to detect oil-containing dressing on salad leaves to assess the energy intake.…”

Section: Related Workmentioning

confidence: 99%

Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation

et al. 2022

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Oil reheating has a significant impact on global health due to its extensive consumption, especially in South Asia, and severe health risks. Nevertheless, food image analysis using multispectral imaging systems(MISs) has not been applied to oil reheating analysis despite their vast application in rapid food quality screening. To that end, the paper discusses the application of a low-cost MSI to estimate the 'reheat cycle count classes' (number of times an oil sample is recursively heated) and identify 'critical classes' at which substantial changes in the oil sample have materialized. Firstly, the reheat cycle count class is estimated with Bhattacharyya distance between the reheated and a pure oil sample as the input. The classification was performed using a support vector machine classifier that resulted in an accuracy of 83.34 % for reheat cycle count identification. Subsequently, an unsupervised clustering procedure was introduced using a modified spectral clustering (SC) algorithm to distinguish critical classes under reheating. In addition, laboratory experiments were performed to ascertain the ramifications of the reheating process with a chemical analysis. The chemical analysis of the coconut oil samples used in the experiment coincided with the chemical analysis results and was statistically significant (p < 0.05). Accordingly, the proposed work closes the gap for using multispectral imaging for oil reheating and proposes a novel algorithm for unsupervised detection of critical property changes in the oil. Hence, the proposed research work is significant in its practical implications, contribution to food image analysis, and unsupervised classification mechanisms.

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“…A. Montes, et al [8] proposed a Low-cost multispectral imaging system for crop monitoring. The author used digital & NIR cameras to calculate orthomosaics images from NIR & RGB images using built-in software FIJI.…”

Section: Related Workmentioning

confidence: 99%

“…UAVs are a module of an unmanned aircraft system (UAS), which consists of a UAV, a groundbased controller, and a method of communications between both. The airlift of UAVs may function with several grades of autonomy: either control by a human with help of remote control or automatically with help of onboard computers denoted as an autopilot [8]. In previous years, a variety of UAV models running on military and civilian applications [9].…”

Section: Introductionmentioning

confidence: 99%

Onion Crop Monitoring with Multispectral Imagery using Deep Neural Network

Din¹,

Naz²,

Zai³

et al. 2021

IJACSA

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The world's growing population leads the government of Pakistan to increase the supply of food for the coming years in a well-organized manner. Feasible agriculture plays a vital role for sustain food production and preserves the environment from any unnecessary chemicals by the use of technology for good management. This research presents the design and development of a multi-spectral imaging system for precision agriculture tasks. This imaging system includes an RGB camera and Pi NoIR camera controlled by a raspberry pi in a drone. The images are captured by Unmanned Aerial Vehicle (UAV) and then send images to the Java application. Images are processed to sharp, resize by application. The Normalized Difference Vegetation Index (NDVI) is calculated to determine the crop health status based on real-time data. The Deep Learning (DL) technique is used to recognize the onion crop growth stage using the captured dataset. We express how to implement a progressive model for the deep neural network to recognize the onion crop growth stage. The performance accuracy of the system for batch size 16 is 96.10% and for batch size 32 is 93.80%.

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Low-cost multispectral imaging system for crop monitoring

Cited by 42 publications

References 18 publications

Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants

Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants

Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation

Onion Crop Monitoring with Multispectral Imagery using Deep Neural Network

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