Sugarcane disease recognition through visible and near-infrared spectroscopy using deep learning assisted continuous wavelet transform-based spectrogram.

Utilizing visible and near-infrared (Vis-NIR) spectroscopy in conjunction with chemometrics methods has been widespread for identifying plant diseases. However, a key obstacle involves the extraction of relevant spectral characteristics. This study aimed to enhance sugarcane disease recognition by combining convolutional neural network (CNN) with continuous wavelet transform (CWT) spectrograms for spectral features extraction within the Vis-NIR spectra (380-1400 nm) to improve the accuracy of sugarcane diseases recognition.

Integrating deep learning with non-destructive thermal imaging for precision guava ripeness determination.

Background: To mitigate post-harvest losses and inform harvesting decisions at the same time as ensuring fruit quality, precise ripeness determination is essential. The complexity arises in assessing guava ripeness as a result of subtle alterations in some varieties during the ripening process, making visual assessment less reliable. The present study proposes a non-destructive method employing thermal imaging for guava ripeness assessment, involving obtaining thermal images of guava samples at different ripeness stages, followed by data pre-processing.

Fast Recognition and Counting Method of Dragon Fruit Flowers and Fruits Based on Video Stream.

Dragon fruit () is a tropical and subtropical fruit that undergoes multiple ripening cycles throughout the year. Accurate monitoring of the flower and fruit quantities at various stages is crucial for growers to estimate yields, plan orders, and implement effective management strategies. However, traditional manual counting methods are labor-intensive and inefficient.

Visible and near-infrared spectroscopic determination of sugarcane chlorophyll content using a modified wavelength selection method for multivariate calibration.

Spectroscopy in the visible and near-infrared region (Vis-NIR) region has proven to be an effective technique for quantifying the chlorophyll contents of plants, which serves as an important indicator of their photosynthetic rate and health status. However, the Vis-NIR spectroscopy analysis confronts a significant challenge concerning the existence of spectral variations and interferences induced by diverse factors. Hence, the selection of characteristic wavelengths plays a crucial role in Vis-NIR spectroscopy analysis.

Evaluation of convolutional neural network for non-destructive detection of imidacloprid and acetamiprid residues in chili pepper (Capsicum frutescens L.) based on visible near-infrared spectroscopy.

Consumption of agricultural products with pesticide residue is risky and can negatively affect health. This study proposed a nondestructive method of detecting pesticide residues in chili pepper based on the combination of visible and near-infrared (VIS/NIR) spectroscopy (400-2498 nm) and deep learning modeling. The obtained spectra of chili peppers with two types of pesticide residues (acetamiprid and imidacloprid) were analyzed using a one-dimensional convolutional neural network (1D-CNN).

Characteristic wavelength optimization for partial least squares regression using improved flower pollination algorithm.

Wavelength selection is crucial to the success of near-infrared (NIR) spectroscopy analysis as it considerably improves the generalization of the multivariate model and reduces model complexity. This study proposes a new wavelength selection method, interval flower pollination algorithm (iFPA), for spectral variable selection in the partial least squares regression (PLSR) model. The proposed iFPA consists of three phases.

New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models.

The proliferation of pathogenic fungi in sugarcane crops poses a significant threat to agricultural productivity and economic sustainability. Early identification and management of sugarcane diseases are therefore crucial to mitigate the adverse impacts of these pathogens. In this study, visible and near-infrared spectroscopy (380-1400 nm) combined with a novel wavelength selection method, referred to as modified flower pollination algorithm (MFPA), was utilized for sugarcane disease recognition.

A non-destructive methodology for determination of cantaloupe sugar content using machine vision and deep learning.

Background: To determine the maturity of cantaloupe, measuring the soluble solid content (SSC) as the indicator of sugar content based on the refractometric index is commonly practised. This method, however, is destructive and limited to a small number of samples. In this study, the coupling of a convolutional neural network (CNN) with machine vision was proposed in detecting the SSC of cantaloupe.

Prediction of tea theanine content using near-infrared spectroscopy and flower pollination algorithm.

In this study, near-infrared (NIR) spectroscopy was exploited for non-destructive determination of theanine content of oolong tea. The NIR spectral data (400-2500 nm) were correlated with the theanine level of 161 tea samples using partial least squares regression (PLSR) with different wavelengths selection methods, including the regression coefficient-based selection, uninformative variable elimination, variable importance in projection, selectivity ratio and flower pollination algorithm (FPA). The potential of using the FPA to select the discriminative wavelengths for PLSR was examined for the first time.

Loss of tumor suppressor KDM6A amplifies PRC2-regulated transcriptional repression in bladder cancer and can be targeted through inhibition of EZH2.

Trithorax-like group complex containing KDM6A acts antagonistically to Polycomb-repressive complex 2 (PRC2) containing EZH2 in maintaining the dynamics of the repression and activation of gene expression through H3K27 methylation. In urothelial bladder carcinoma, (a H3K27 demethylase) is frequently mutated, but its functional consequences and therapeutic targetability remain unknown. About 70% of mutations resulted in a total loss of expression and a consequent loss of demethylase function in this cancer type.

Fumarate Hydratase-deficient Cell Line NCCFH1 as a New In Vitro Model of Hereditary Papillary Renal Cell Carcinoma Type 2.

Background/aim: Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) is a rare autosomal dominant disorder characterized by fumarate hydratase (FH) gene mutation. It is associated with the development of very aggressive kidney tumors, characterized by early onset and high metastatic potential, and has no effective therapy. The aim of the study was to establish a new preclinical platform for investigating morphogenetic and metabolic features, and alternative therapy of metastatic hereditary papillary renal cell carcinoma type 2 (PRCC2).

Adaptive cuckoo search algorithm for unconstrained optimization.

Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.

Reliable epileptic seizure detection using an improved wavelet neural network.

Background: Electroencephalogram (EEG) signal analysis is indispensable in epilepsy diagnosis as it offers valuable insights for locating the abnormal distortions in the brain wave. However, visual interpretation of the massive amounts of EEG signals is time-consuming, and there is often inconsistent judgment between experts.

Aims: This study proposes a novel and reliable seizure detection system, where the statistical features extracted from the discrete wavelet transform are used in conjunction with an improved wavelet neural network (WNN) to identify the occurrence of seizures.

Exome sequencing of liver fluke-associated cholangiocarcinoma.

Opisthorchis viverrini-related cholangiocarcinoma (CCA), a fatal bile duct cancer, is a major public health concern in areas endemic for this parasite. We report here whole-exome sequencing of eight O. viverrini-related tumors and matched normal tissue.

Wavelet neural networks applied to pulping of oil palm fronds.

In the organosolv pulping of the oil palm fronds, the influence of the operational variables of the pulping reactor (viz. cooking temperature and time, ethanol and NaOH concentration) on the properties of the resulting pulp (yield and kappa number) and paper sheets (tensile index and tear index) was investigated using a wavelet neural network model. The experimental results with error less than 0.