A multi-verse optimizer-based CNN-BiLSTM pixel-level detection model for peanut aflatoxins.

Peanuts are highly susceptible to contamination by aflatoxins, posing a significant threat to human health. This study aims to enhance the accuracy of pixel-level aflatoxin detection in hyperspectral images using an optimized deep learning method. This study developed a CNN-BiLSTM fusion model optimized by the Multi-Verse Optimizer (MVO) algorithm, specifically designed to detect aflatoxins with high precision.

Precision Detection of Salt Stress in Soybean Seedlings Based on Deep Learning and Chlorophyll Fluorescence Imaging.

Soil salinization poses a critical challenge to global food security, impacting plant growth, development, and crop yield. This study investigates the efficacy of deep learning techniques alongside chlorophyll fluorescence (ChlF) imaging technology for discerning varying levels of salt stress in soybean seedlings. Traditional methods for stress identification in plants are often laborious and time-intensive, prompting the exploration of more efficient approaches.

From Organelle Morphology to Whole-Plant Phenotyping: A Phenotypic Detection Method Based on Deep Learning.

The analysis of plant phenotype parameters is closely related to breeding, so plant phenotype research has strong practical significance. This paper used deep learning to classify from the macro (plant) to the micro level (organelle). First, the multi-output model identifies Arabidopsis accession lines and regression to predict Arabidopsis's 22-day growth status.

Spectral intelligent detection for aflatoxin B1 via contrastive learning based on Siamese network.

Aflatoxins, harmful substances found in peanuts, corn, and their derivatives, pose significant health risks. Addressing this, the presented research introduces an innovative MSGhostDNN model, merging contrastive learning with multi-scale convolutional networks for precise aflatoxin detection. The method significantly enhances feature discrimination, achieving an impressive 97.

Identification of Soybean Mutant Lines Based on Dual-Branch CNN Model Fusion Framework Utilizing Images from Different Organs.

The accurate identification and classification of soybean mutant lines is essential for developing new plant varieties through mutation breeding. However, most existing studies have focused on the classification of soybean varieties. Distinguishing mutant lines solely by their seeds can be challenging due to their high genetic similarities.

Quantitative detection of Aflatoxin B1 by subpixel CNN regression.

Aflatoxin is a highly toxic substance dispersed in peanuts, which seriously harms the health of humans and animals. In this paper, we propose a new method for aflatoxin B1(AFB1) detection inspired by quantitative remote sensing. Firstly, we obtained the relative content of AFB1 at the sub-pixel level by subpixel decomposition (endmember extraction, nonnegative matrix decomposition).

Aflatoxin rapid detection based on hyperspectral with 1D-convolution neural network in the pixel level.

Aflatoxin is commonly exists in moldy foods, it is classified as a class one carcinogen by the World Health Organization. In this paper, we used one dimensional convolution neural network (1D-CNN) to classify whether a pixel contains aflatoxin. Firstly we found the best combination of 1D-CNN parameters were epoch = 30, learning rate = 0.

Analysis of Behavior Trajectory Based on Deep Learning in Ammonia Environment for Fish.

Ammonia can be produced by the respiration and excretion of fish during the farming process, which can affect the life of fish. In this paper, to research the behavior of fish under different ammonia concentration and make the corresponding judgment and early warning for the abnormal behavior of fish, the different ammonia environments are simulated by adding the ammonium chloride into the water. Different from the existing methods of directly artificial observation or artificial marking, this paper proposed a recognition and analysis of behavior trajectory approach based on deep learning.

Aflatoxin contaminated degree detection by hyperspectral data using band index.

Aflatoxin is a highly toxic and carcinogenic substance with fluorescence characteristic. To explore the feasibility of detection the degree of aflatoxin contamination using hyperspectral imaging technology, we proposed a machine learning detection method based on support vector machine (SVM) combining band index and narrow band. First, five concentrations of aflatoxin solutions (10ug/L, 20ug/L, 50ug/L, 100ug/L and 10 mg/L) were prepared and dripped onto the surface of different peanut kernels.

Recognition pest by image-based transfer learning.

Background: Plant pests mainly refers to insects and mites that harm crops and products. There are a wide variety of plant pests, with wide distribution, fast reproduction and large quantity, which directly causes serious losses to crops. Therefore, pest recognition is very important for crops to grow healthily, and this in turn affects crop yields and quality.

Experimental and Theoretical Study on the Synergistic Inhibition Effect of Pyridine Derivatives and Sulfur-Containing Compounds on the Corrosion of Carbon Steel in CO₂-Saturated 3.5 wt.% NaCl Solution.

The corrosion inhibition performance of pyridine derivatives (4-methylpyridine and its quaternary ammonium salts) and sulfur-containing compounds (thiourea and mercaptoethanol) with different molar ratios on carbon steel in CO₂-saturated 3.5 wt.% NaCl solution was investigated by weight loss, potentiodynamic polarization, electrochemical impedance spectroscopy, and scanning electron microscopy.

Image features and DUS testing traits for peanut pod variety identification and pedigree analysis.

Background: DUS (Distinctness, Uniformity and Stability) testing of new varieties is an important method for peanut germplasm evaluation and identification of varieties. In order to verify the feasibility of variety identification for peanut DUS testing based on image processing, 2000 peanut pod images from 20 varieties were obtained by a scanner. Initially, six DUS testing traits were quantified using a mathematical method based on image processing technology, and then, size, shape, color and texture features (total 31) were also extracted.

Single seed precise sowing of maize using computer simulation.

In order to test the feasibility of computer simulation in field maize planting, the selection of the method of single seed precise sowing in maize is studied based on the quadratic function model Y = A×(D-Dm)2+Ym, which depicts the relationship between maize yield and planting density. And the advantages and disadvantages of the two planting methods under the condition of single seed sowing are also compared: Method 1 is optimum density planting, while Method 2 is the ideal seedling emergence number planting. It is found that the yield reduction rate and yield fluctuation of Method 2 are all lower than those of Method 1.

Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA.

To investigate the feasibility of identification of qualified and adulterated oil product using hyperspectral imaging(HIS) technique, a novel feature set based on quantized histogram matrix (QHM) and feature selection method using improved kernel independent component analysis (iKICA) is proposed for HSI. We use UV and Halogen excitations in this study. Region of interest(ROI) of hyperspectral images of 256 oil samples from four varieties are obtained within the spectral region of 400-720nm.

Hybrid Progressive Algorithm to Recognize Type II Diabetic Based on Hair Mineral Element Contents.

In this paper, a hybrid progressive algorithm to recognize type II diabetic based on hair mineral element levels is proposed. Hair samples of 244 cases (Table 1) are collected from 51 healthy persons (one case each person), 47 unchecked diabetics (one case each person) and 73 checked diabetics (two cases each person). 8 hair elements (Mg, Ca, Fe, Cu, Zn, Se, Cr and Mn) are measured.