Hyperspectral imaging coupled with multivariate analysis and artificial intelligence
to the classification of maize kernels

Alimohammadi, Fariba; Rasekh, Mansour; Sayyah, Amir Hosein Afkari; Abbaspour‐Gilandeh, Yousef; Karami, Hamed; Sharabiani, Vali Rasooli; Fioravanti, Ambra; Gancarz, Marek; Findura, Pavol; Kwaśniewski, D.

doi:10.31545/intagr/147227

Cited by 22 publications

(19 citation statements)

References 45 publications

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“…For the six-group classification, based on the type of extract, the polynomial and RBF functions had a classification accuracy of 98.9 in the C-SVM method, while in the Nu-SVM method the classification accuracy of linear functions and RBF was 100% in learning and 98.9% in validation. Similar results have been reported for other crops, such as grape leaves [ 44 ], fruit juices [ 45 ], essential oils [ 4 , 16 ], coffee bean [ 46 ], corn [ 47 ], and cucumbers [ 48 ].…”

Section: Discussionsupporting

confidence: 84%

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes

et al. 2022

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Five potato varieties were studied using an electronic nose with nine MOS sensors. Parameters measured included carbohydrate content, sugar level, and the toughness of the potatoes. Routine tests were carried out while the signals for each potato were measured, simultaneously, using an electronic nose. The signals obtained indicated the concentration of various chemical components. In addition to support vector machines (SVMs that were used for the classification of the samples, chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models for sugar and carbohydrates. The predictive power of the regression models was characterized by a coefficient of determination (R2), a root-mean-square error of prediction (RMSEP), and offsets. PLSR was able to accurately model the relationship between the smells of different types of potatoes, sugar, and carbohydrates. The highest and lowest accuracy of models for predicting sugar and carbohydrates was related to Marfona potatoes and Sprite cultivar potatoes. In general, in all cultivars, the accuracy in predicting the amount of carbohydrates was somewhat better than the accuracy in predicting the amount of sugar. Moreover, the linear function had 100% accuracy for training and validation in the C-SVM method for classification of five potato groups. The electronic nose could be used as a fast and non-destructive method for detecting different potato varieties. Researchers in the food industry will find this method extremely useful in selecting the desired product and samples.

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Section: Discussionsupporting

confidence: 84%

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes

et al. 2022

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“…Similar studies have focused on large-scale farming systems that have employed hyperspectral imaging, thermal imaging, soil moisture, rainfall estimates, etc. [ 3 , 4 , 6 , 13 , 16 , 17 , 21 , 25 , 87 ]. These studies are required to fuse data from multiple satellite sources to identify similar crops in a given region.…”

Section: Discussionmentioning

confidence: 99%

“…RS is widely used in precision agricultural (PA) applications as it is considered a reliable source for extracting phenological information about crops [ 7 , 8 , 9 , 10 ]. The availability of high spectral, spatial, and temporal RS data, including multi-spectral [ 11 , 12 ], hyperspectral [ 13 ], and synthetic aperture radar (SAR) [ 6 , 14 , 15 , 16 , 17 ] have opened new possibilities in crop-type mapping [ 3 , 18 , 19 , 20 , 21 ], crop health [ 22 ] and yield estimation [ 4 , 23 , 24 , 25 ]. In the early 21st century, Landsat and moderate resolution imaging spectroradiometer (MODIS) multi-spectral data were relied on for crop types [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery

Khan

Gillani

Jamal

et al. 2023

Sensors

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Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essential role in monitoring crops, estimating yields, and maintaining smooth food supplies. However, these maps are not available for developing countries until crops have matured and are about to be harvested. The use of remote sensing for accurate crop-type mapping in the first few weeks of sowing remains challenging. Smallholder farming systems and diverse crop types further complicate the challenge. For this study, a ground-based survey is carried out to map fields by recording the coordinates and planted crops in respective fields. The time-series images of the mapped fields are acquired from the Sentinel-2 satellite. A deep learning-based long short-term memory network is used for the accurate mapping of crops at an early growth stage. Results show that staple crops, including rice, wheat, and sugarcane, are classified with 93.77% accuracy as early as the first four weeks of sowing. The proposed method can be applied on a large scale to effectively map crop types for smallholder farms at an early stage, allowing the authorities to plan a seamless availability of food.

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“…Multivariate statistical methods applied to hyperspectral data analysis provide a valuable and precise understanding of various plant factors, such as productivity, nutrient imbalances, temperature stress, changes in the xanthophyll cycle, and mesophyll cell structure [10,19,20]. In this sense, new methods are being developed to monitor leaf pigment accumulation during plant growth and development, and spectral reflectance is one of the most widely used techniques, although it may not always be reliable due to the overlapping spectra of different pigments and intrinsic variations in leaf structure [21][22][23]. To overcome these limitations, the use of spectral absorption has shown promising results in the remote sensing of leaf pigments [21,22,24].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach

Falcioni

Antunes

Demattê

et al. 2023

Sensors

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Leaf optical properties can be used to identify environmental conditions, the effect of light intensities, plant hormone levels, pigment concentrations, and cellular structures. However, the reflectance factors can affect the accuracy of predictions for chlorophyll and carotenoid concentrations. In this study, we tested the hypothesis that technology using two hyperspectral sensors for both reflectance and absorbance data would result in more accurate predictions of absorbance spectra. Our findings indicated that the green/yellow regions (500–600 nm) had a greater impact on photosynthetic pigment predictions, while the blue (440–485 nm) and red (626–700 nm) regions had a minor impact. Strong correlations were found between absorbance (R2 = 0.87 and 0.91) and reflectance (R2 = 0.80 and 0.78) for chlorophyll and carotenoids, respectively. Carotenoids showed particularly high and significant correlation coefficients using the partial least squares regression (PLSR) method (R2C = 0.91, R2cv = 0.85, and R2P = 0.90) when associated with hyperspectral absorbance data. Our hypothesis was supported, and these results demonstrate the effectiveness of using two hyperspectral sensors for optical leaf profile analysis and predicting the concentration of photosynthetic pigments using multivariate statistical methods. This method for two sensors is more efficient and shows better results compared to traditional single sensor techniques for measuring chloroplast changes and pigment phenotyping in plants.

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Hyperspectral imaging coupled with multivariate analysis and artificial intelligence to the classification of maize kernels

Cited by 22 publications

References 45 publications

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes

Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery

A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach

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