Testing the suitability of automated machine learning, hyperspectral imaging and CIELAB color space for proximal in situ fertilization level classification

Malounas, Ioannis; Lentzou, Diamanto; Xanthopoulos, Georgios; Fountas, Spyros

doi:10.1016/j.atech.2024.100437

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…Through the application of computer vision in the fruit processing industry, tasks like quality classification and gradation are automated. [22] concentrates on the identification of rotten or fresh apples by detecting the peel defects via deep learning-based semantic segmentation. The research uses UNet and its improved version, En-UNet, and the latter attain the training and validation accuracies of 97.46% and 97.54%, respectively, which is a lot less than 95%.36% for UNet.…”

Section: Literature Reviewmentioning

confidence: 99%

Apple Perfection: Assessing Apple Quality with Waterwheel Plant Algorithm for Feature Selection and Logistic Regression for Classification

Ibrahim,

Khodadadi,

Khodadadi

et al. 2024

Journal of Artificial Intelligence in Engineering Practice

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This study concentrates on the evaluation of apple quality, which is a vital part of the agricultural industry. The quality of apples is examined through several factors, such as the cultivation techniques, the harvesting methods, and the post-harvest procedures. The dataset, titled "Apple Perfection," contains important characteristics such as the size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and overall quality of the apple. To make the apple quality prediction more accurate, we used different feature selection algorithms, mainly the binary Waterwheel Plant Algorithm (bWWPA), which, in fact, had the lowest average error of 0.52153, and several of the types of classification models, especially Logistic Regression, which had the highest accuracy of 0.88625. The attribute selection process found the most important attributes, which cut down the dimensionality, and hence, the model performance became better. The results of the study show that the combination of bWWPA for feature selection and logistic regression for classification can predict apple quality with high accuracy. This way of dealing with the problem gives us information that is useful for the improvement of the cultivation techniques and the post-harvest handling to the extent that we will be able to have the best quality apples. The findings of this research have a great impact on the farming industry, meaning a strong way to evaluate the quality of apples.

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Section: Literature Reviewmentioning

confidence: 99%

Apple Perfection: Assessing Apple Quality with Waterwheel Plant Algorithm for Feature Selection and Logistic Regression for Classification

Ibrahim,

Khodadadi,

Khodadadi

et al. 2024

Journal of Artificial Intelligence in Engineering Practice

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show abstract

“…The color space transformation carried out is from the Red-Green-Blue (RGB) image into the CIE L* a* b* color format, also known as CIELAB. The CIELAB color system was designed to provide color representation that is more consistent and more closely related to human perception of color (Malounas et al, 2024). The CIELAB color space transformation offers several advantages, primarily because it better reflects the way the human eye sees and perceives color (Baek et al, 2022).…”

Section: Transforming Color Spacementioning

confidence: 99%

Identification of Potato Leaf Diseases Using Artificial Neural Networks With Extreme Learning Machine Algorithm

Erkamim,

Septarini,

Tonggiroh

et al. 2024

pilar

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Potato plants have an important role in providing a source of carbohydrates for society. However, potato production is often threatened by various plant diseases, such as leaf disease, which can cause a decrease in yields. Identification of diseases on potato leaves is currently mostly done by farmers manually, so it is not always efficient and accurate. So the aim of this research is to identify diseases on potato leaves with artificial neural networks using the ELM (Extreme Learning Machine) approach and the GLCM (Gray Level Co-Occurrence Matrix) method for feature extraction. The GLCM approach functions to obtain texture features on objects by measuring how often certain pairs of pixel intensities appear together at various distances and directions in the image. Meanwhile, the ELM algorithm is used for image identification by adopting a one-time training method without iteration, which involves randomly determining weights and biases in hidden layers, thus allowing training to be carried out quickly and efficiently. Evaluation of the model by looking for the level of accuracy produces a value of 84.667%. The results show that the model developed is capable of accurate identification.

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Prediction of Salicornia europaea L. biomass using a computer vision system to distinguish different salt-tolerant populations

Cárdenas-Pérez,

Grigore,

Piernik

2024

BMC Plant Biol

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Background Salicornia europaea L. is emerging as a versatile crop halophyte, requiring a low-cost, non-destructive method for salt tolerance classification to aid selective breeding. We propose using a computer vision system (CVS) with multivariate analysis to classify S. europaea based on morphometric and colour traits to predict plant biomass and the salinity in their substrate. Results A trial and validation set of 96 and 24 plants from 2 populations confirmed the efficacy. CVS and multivariate analysis evaluated the plants by morphometric traits and CIELab colour variability. Through Pearson analysis, the strongest correlations were between biomass fresh weight (FW) vs. projected area (PA) (0.91) and anatomical cross-section (ACS) vs. shoot diameter (Sd) (0.94). The PA and FW correlation retrieved different equation fits between lower and higher salt-tolerant populations (R2 = 0.93 for linear and 0.90 for 2nd-degree polynomial), respectively. The higher salt-tolerant reached a maximum biomass PA at 400 mM NaCl, while the lower salt-tolerant produced less under 200 and 400 mM. A second Pearson correlation and PCA described sample variability with 80% reliability using only morphometric-colour parameters. Multivariate discriminant analysis (MDA) demonstrated that the method correctly classifies plants (90%) depending on their salinity level and tolerance, which was validated with 100% effectiveness. Through multiple linear regression, a predictive model successfully estimated biomass production by PA, and a second model predicted the salinity substrate (Sal.s.) where the plants thrive. Plants' Sd and height influenced PA prediction, while Sd and colour difference (ΔE1) influenced Sal.s. Models validation of actual vs. predicted values showed a R2 of 0.97 and 0.90 for PA, and 0.95 and 0.97 for Sal.s. for lower and higher salt-tolerant, respectively. This outcome confirms the method as a cost-effective tool for managing S. europaea breeding. Conclusions The CVS effectively extracted morphological and colour features from S. europaea cultivated at different salinity levels, enabling classification and plant sorting through image and multivariate analysis. Biomass and salinity substrate were accurately predicted by modelling non-destructive parameters. Enhanced by AI, machine learning and smartphone technology, this method shows great potential in ecology, bio-agriculture, and industry.

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Testing the suitability of automated machine learning, hyperspectral imaging and CIELAB color space for proximal in situ fertilization level classification

Cited by 6 publications

References 30 publications

Apple Perfection: Assessing Apple Quality with Waterwheel Plant Algorithm for Feature Selection and Logistic Regression for Classification

Apple Perfection: Assessing Apple Quality with Waterwheel Plant Algorithm for Feature Selection and Logistic Regression for Classification

Identification of Potato Leaf Diseases Using Artificial Neural Networks With Extreme Learning Machine Algorithm

Prediction of Salicornia europaea L. biomass using a computer vision system to distinguish different salt-tolerant populations

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