Development of an Innovative Optoelectronic Nose for Detecting Adulteration in Quince Seed Oil.

In this study, an innovative odor imaging system capable of detecting adulteration in quince seed edible oils mixed with sunflower oil and sesame oil based on their volatile organic compound (VOC) profiles was developed. The system comprises a colorimetric sensor array (CSA), a data acquisition unit, and a machine learning algorithm for identifying adulterants. The CSA was created using a method that involves applying a mixture of six different pH indicators (methyl violet, chlorophenol red, Nile blue, methyl orange, alizarin, cresol red) onto a Thin Layer Chromatography (TLC) silica gel plate. Subsequently, difference maps were generated by subtracting the "initial" image from the "final" image, with the resulting color changes being converted into digital data, which were then further analyzed using Principal Component Analysis (PCA). Following this, a Support Vector Machine was employed to scrutinize quince seed oil that had been adulterated with varying proportions of sunflower oil and sesame oil. The classifier was progressively supplied with an increasing number of principal components (PCs), starting from one and incrementally increasing up to five. Each time, the classifier was optimized to determine the hyperparameters utilizing a random search algorithm. With one to five PCs, the classification error accounted for a range of 37.18% to 1.29%. According to the results, this novel system is simple, cost-effective, and has potential applications in food quality control and consumer protection.

Identification and Classification of Downy Mildew Severity Stages in Watermelon Utilizing Aerial and Ground Remote Sensing and Machine Learning.

Remote sensing and machine learning (ML) could assist and support growers, stakeholders, and plant pathologists determine plant diseases resulting from viral, bacterial, and fungal infections. Spectral vegetation indices (VIs) have shown to be helpful for the indirect detection of plant diseases. The purpose of this study was to utilize ML models and identify VIs for the detection of downy mildew (DM) disease in watermelon in several disease severity (DS) stages, including low, medium (levels 1 and 2), high, and very high. Hyperspectral images of leaves were collected in the laboratory by a benchtop system (380-1,000 nm) and in the field by a UAV-based imaging system (380-1,000 nm). Two classification methods, multilayer perceptron (MLP) and decision tree (DT), were implemented to distinguish between healthy and DM-affected plants. The best classification rates were recorded by the MLP method; however, only 62.3% accuracy was observed at low disease severity. The classification accuracy increased when the disease severity increased (e.g., 86-90% for the laboratory analysis and 69-91% for the field analysis). The best wavelengths to differentiate between the DS stages were selected in the band of 531 nm, and 700-900 nm. The most significant VIs for DS detection were the chlorophyll green (Cl green), photochemical reflectance index (PRI), normalized phaeophytinization index (NPQI) for laboratory analysis, and the ratio analysis of reflectance spectral chlorophyll-a, b, and c (RARSa, RASRb, and RARSc) and the Cl green in the field analysis. Spectral VIs and ML could enhance disease detection and monitoring for precision agriculture applications.

Specific Fluorescence in Situ Hybridization (FISH) Test to Highlight Colonization of Xylem Vessels by Xylella fastidiosa in Naturally Infected Olive Trees (Olea europaea L.).

The colonization behavior of the Xylella fastidiosa strain CoDiRO, the causal agent of olive quick decline syndrome (OQDS), within the xylem of Olea europaea L. is still quite controversial. As previous literature suggests, even if xylem vessel occlusions in naturally infected olive plants were observed, cell aggregation in the formation of occlusions had a minimal role. This observation left some open questions about the whole behavior of the CoDiRO strain and its actual role in OQDS pathogenesis. In order to evaluate the extent of bacterial infection in olive trees and the role of bacterial aggregates in vessel occlusions, we tested a specific fluorescence in situ hybridization (FISH) probe (KO 210) for X. fastidiosa and quantified the level of infection and vessel occlusion in both petioles and branches of naturally infected and non-infected olive trees. All symptomatic petioles showed colonization by X. fastidiosa, especially in the larger innermost vessels. In several cases, the vessels appeared completely occluded by a biofilm containing bacterial cells and extracellular matrix and the frequent colonization of adjacent vessels suggested a horizontal movement of the bacteria. Infected symptomatic trees had 21.6 ± 10.7% of petiole vessels colonized by the pathogen, indicating an irregular distribution in olive tree xylem. Thus, our observations point out the primary role of the pathogen in olive vessel occlusions. Furthermore, our findings indicate that the KO 210 FISH probe is suitable for the specific detection of X. fastidiosa.

X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion.

We have developed a vision-based program to detect symptoms of Olive Quick Decline Syndrome (OQDS) on leaves of Olea europaea L. infected by Xylella fastidiosa, named X-FIDO (Xylella FastIdiosa Detector for O. europaea L.). Previous work predicted disease from leaf images with deep learning but required a vast amount of data which was obtained via crowd sourcing such as the PlantVillage project. This approach has limited applicability when samples need to be tested with traditional methods (i.e., PCR) to avoid incorrect training input or for quarantine pests which manipulation is restricted. In this paper, we demonstrate that transfer learning can be leveraged when it is not possible to collect thousands of new leaf images. Transfer learning is the re-application of an already trained deep learner to a new problem. We present a novel algorithm for fusing data at different levels of abstraction to improve performance of the system. The algorithm discovers low-level features from raw data to automatically detect veins and colors that lead to symptomatic leaves. The experiment included images of 100 healthy leaves, 99 X. fastidiosa-positive leaves and 100 X. fastidiosa-negative leaves with symptoms related to other stress factors (i.e., abiotic factors such as water stress or others diseases). The program detects OQDS with a true positive rate of 98.60 ± 1.47% in testing, showing great potential for image analysis for this disease. Results were obtained with a convolutional neural network trained with the stochastic gradient descent method, and ten trials with a 75/25 split of training and testing data. This work shows potential for massive screening of plants with reduced diagnosis time and cost.