Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves

Borhan, Md. Saidul; Panigrahi, Suranjan; Satter, M.A.; Gu, Hanming

doi:10.1016/j.inpa.2017.07.005

Cited by 23 publications

(20 citation statements)

References 20 publications

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“…All VI approaches performed better in monitoring the growth and predicting the yield of maize, which is in accordance with previous studies where the VI had been proven to have great potential abilities in agricultural and ecological applications [ 44 , 45 , 46 , 47 ]. The MRBVI performed relatively better than the other VIs, and this can be ascribed to the fact that the structure function of the MRBVI emphasized the response of the red band, and it was believed to have a higher correlation with chlorophyll contents than the green and blue bands [ 48 , 49 ]. The proposed MRBVI had the closest relationships with chlorophyll contents measured using SPAD-502, and this was in agreement with previous studies that had found that the red band had greater sensitivity to the chlorophyll contents [ 48 , 49 ].…”

Section: Discussionmentioning

confidence: 99%

“…The MRBVI performed relatively better than the other VIs, and this can be ascribed to the fact that the structure function of the MRBVI emphasized the response of the red band, and it was believed to have a higher correlation with chlorophyll contents than the green and blue bands [ 48 , 49 ]. The proposed MRBVI had the closest relationships with chlorophyll contents measured using SPAD-502, and this was in agreement with previous studies that had found that the red band had greater sensitivity to the chlorophyll contents [ 48 , 49 ]. Thus, the extraction of the conditions of the growth of maize was improved by adding the content of the red band, which was believed to have a higher correlation with chlorophyll contents than the green and blue bands.…”

Section: Discussionmentioning

confidence: 99%

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Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

Guo

Wang

et al. 2020

Sensors

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The vegetation index (VI) has been successfully used to monitor the growth and to predict the yield of agricultural crops. In this paper, a long-term observation was conducted for the yield prediction of maize using an unmanned aerial vehicle (UAV) and estimations of chlorophyll contents using SPAD-502. A new vegetation index termed as modified red blue VI (MRBVI) was developed to monitor the growth and to predict the yields of maize by establishing relationships between MRBVI- and SPAD-502-based chlorophyll contents. The coefficients of determination (R2s) were 0.462 and 0.570 in chlorophyll contents’ estimations and yield predictions using MRBVI, and the results were relatively better than the results from the seven other commonly used VI approaches. All VIs during the different growth stages of maize were calculated and compared with the measured values of chlorophyll contents directly, and the relative error (RE) of MRBVI is the lowest at 0.355. Further, machine learning (ML) methods such as the backpropagation neural network model (BP), support vector machine (SVM), random forest (RF), and extreme learning machine (ELM) were adopted for predicting the yields of maize. All VIs calculated for each image captured during important phenological stages of maize were set as independent variables and the corresponding yields of each plot were defined as dependent variables. The ML models used the leave one out method (LOO), where the root mean square errors (RMSEs) were 2.157, 1.099, 1.146, and 1.698 (g/hundred grain weight) for BP, SVM, RF, and ELM. The mean absolute errors (MAEs) were 1.739, 0.886, 0.925, and 1.356 (g/hundred grain weight) for BP, SVM, RF, and ELM, respectively. Thus, the SVM method performed better in predicting the yields of maize than the other ML methods. Therefore, it is strongly suggested that the MRBVI calculated from images acquired at different growth stages integrated with advanced ML methods should be used for agricultural- and ecological-related chlorophyll estimation and yield predictions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

Guo

Wang

et al. 2020

Sensors

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“…We also compare the performance of the proposed method with numerous existing vision-based plant-N nutrition estimation methods. These methods include [23,[69][70][71][72][73][74][75][76], and their performance comparison is presented in Table 4. In [69], Liu et al investigated the use of SPAD-502 (Minolta, Japan) for nitrogen estimation in spinach plants.…”

Section: Resultsmentioning

confidence: 99%

A Computer-Vision-Based Approach for Nitrogen Content Estimation in Plant Leaves

et al. 2021

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Nitrogen is an essential nutrient element required for optimum crop growth and yield. If a specific amount of nitrogen is not applied to crops, their yield is affected. Estimation of nitrogen level in crops is momentous to decide the nitrogen fertilization in crops. The amount of nitrogen in crops is measured through different techniques, including visual inspection of leaf color and texture and by laboratory analysis of plant leaves. Laboratory analysis-based techniques are more accurate than visual inspection, but they are costly, time-consuming, and require skilled laboratorian and precise equipment. Therefore, computer-based systems are required to estimate the amount of nitrogen in field crops. In this paper, a computer vision-based solution is introduced to solve this problem as well as to help farmers by providing an easier, cheaper, and faster approach for measuring nitrogen deficiency in crops. The system takes an image of the crop leaf as input and estimates the amount of nitrogen in it. The image is captured by placing the leaf on a specially designed slate that contains the reference green and yellow colors for that crop. The proposed algorithm automatically extracts the leaf from the image and computes its color similarity with the reference colors. In particular, we define a green color value (GCV) index from this analysis, which serves as a nitrogen indicator. We also present an evaluation of different color distance models to find a model able to accurately capture the color differences. The performance of the proposed system is evaluated on a Spinacia oleracea dataset. The results of the proposed system and laboratory analysis are highly correlated, which shows the effectiveness of the proposed system.

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“…Moreover, the SPAD values were different in different measurement spots [19], which is not conducive to the practice of precision agriculture. To measure the spectral data of a canopy area or whole potato plant without any contact or damage, some researchers developed spectral imaging sensors [20][21][22]. For instance, Borhan and Panigrah [21] developed a CCD sensor to collect indoor images at 550 and 700 nm for detecting the SPAD value of potato.…”

Section: Introductionmentioning

confidence: 99%

“…To measure the spectral data of a canopy area or whole potato plant without any contact or damage, some researchers developed spectral imaging sensors [20][21][22]. For instance, Borhan and Panigrah [21] developed a CCD sensor to collect indoor images at 550 and 700 nm for detecting the SPAD value of potato. And the determination coefficient of calibration set was 0.80.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection on SPAD Value of Potato Plant Using an In-Field Spectral Imaging Sensor System

Liu

Sun

2020

Sensors

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In this study, a SPAD value detection system was developed based on a 25-wavelength spectral sensor to give a real-time indication of the nutrition distribution of potato plants in the field. Two major advantages of the detection system include the automatic segmentation of spectral images and the real-time detection of SPAD value, a recommended indicating parameter of chlorophyll content. The modified difference vegetation index (MDVI) linking the Otsu algorithm (OTSU) and the connected domain-labeling (CDL) method (MDVI–OTSU–CDL) is proposed to accurately extract the potato plant. Additionally, the segmentation accuracy under different modified coefficients of MDVI was analyzed. Then, the reflectance of potato plants was extracted by the segmented mask images. The partial least squares (PLS) regression was employed to establish the SPAD value detection model based on sensitive variables selected using the uninformative variable elimination (UVE) algorithm. Based on the segmented spectral image and the UVE–PLS model, the visualization distribution map of SPAD value was drawn by pseudo-color processing technology. Finally, the testing dataset was employed to measure the stability and practicality of the developed detection system. This study provides a powerful support for the real-time detection of SPAD value and the distribution of crops in the field.

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Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves

Cited by 23 publications

References 20 publications

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

A Computer-Vision-Based Approach for Nitrogen Content Estimation in Plant Leaves

Real-Time Detection on SPAD Value of Potato Plant Using an In-Field Spectral Imaging Sensor System

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