Predictive Neural Network Modeling for Almond Harvest Dust Control.

This study introduces a neural network-based approach to predict dust emissions, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), this research predicted PM2.5 emissions by analyzing key operational parameters of an advanced almond harvester. Preprocessing steps like outlier removal and normalization were employed to refine the dataset for training. The network's architecture was designed with two hidden layers and optimized using tanh activation and MSE loss functions through the Adam algorithm, striking a balance between model complexity and predictive accuracy. The model was trained on extensive field data from an almond pickup system, including variables like brush speed, angular velocity, and harvester forward speed. The results demonstrate a notable predictive accuracy of the FNN model, with a mean squared error (MSE) of 0.02 and a mean absolute error (MAE) of 0.01, indicating high precision in forecasting PM2.5 levels. By integrating machine learning with agricultural practices, this research provides a significant tool for environmental management in almond production, offering a method to reduce harmful emissions while maintaining operational efficiency. This model presents a solution for the almond industry and sets a precedent for applying predictive analytics in sustainable agriculture.

Design of an Integrated Controller for a Sweeping Mechanism of a Low-Dust Almond Pickup Machine.

California is the world's biggest producer and exporter of almonds. Currently, the sweeping of almonds during the harvest creates a significant amount of dust, causing air pollution in the neighboring urban areas. A low-dust sweeping system was designed to reduce the dust during the sweeping of almonds in the orchard. The system includes a feedback control system to control the sweeper brushes' height and their angular velocity by adjusting the forward velocity of the harvester and the brushes' rotational speeds to avoid any extra overlapping sweeping, which increases dust generation. The governing kinematic equations for sweepers' angular velocity and vehicle forward speed were derived. The feedback controllers for synchronizing these speeds were designed to optimize brush/dust contact to minimize dust generation. The sweepers' height controller was also designed to stabilize the gap between the brushes and the orchard floor and track the road trajectory. Controllers were simulated and tuned for a fast response for agricultural applications with less than a second response delay. Results showed that the designed system has acceptable performance and generates low amounts of dust within the acceptable range of California ambient air quality standards.

Application of ozone for degradation of mycotoxins in food: A review.

Mycotoxins such as aflatoxins (AFs), ochratoxin A (OTA) fumonisins (FMN), deoxynivalenol (DON), zearalenone (ZEN), and patulin are stable at regular food process practices. Ozone (O3 ) is a strong oxidizer and generally considered as a safe antimicrobial agent in food industries. Ozone disrupts fungal cells through oxidizing sulfhydryl and amino acid groups of enzymes or attacks the polyunsaturated fatty acids of the cell wall. Fusarium is the most sensitive mycotoxigenic fungi to ozonation followed by Aspergillus and Penicillium. Studies have shown complete inactivation of Fusarium and Aspergillus by O3 gas. Spore germination and toxin production have also been reduced after ozone fumigation. Both naturally and artificially, mycotoxin-contaminated samples have shown significant mycotoxin reduction after ozonation. Although the mechanism of detoxification is not very clear for some mycotoxins, it is believed that ozone reacts with the functional groups in the mycotoxin molecules, changes their molecular structures, and forms products with lower molecular weight, less double bonds, and less toxicity. Although some minor physicochemical changes were observed in some ozone-treated foods, these changes may or may not affect the use of the ozonated product depending on the further application of it. The effectiveness of the ozonation process depends on the exposure time, ozone concentration, temperature, moisture content of the product, and relative humidity. Due to its strong oxidizing property and corrosiveness, there are strict limits for O3 gas exposure. O3 gas has limited penetration and decomposes quickly. However, ozone treatment can be used as a safe and green technology for food preservation and control of contaminants.

Developing Citrus Huanglongbing (HLB) Management Strategies Based on the Severity of Symptoms in HLB-Endemic Citrus-Producing Regions.

Citrus Huanglongbing (HLB), also known as greening, is a destructive disease caused by the fastidious, phloem-colonizing bacteria Candidatus Liberibacter spp.; 'Ca. Liberibacter asiaticus' (Las) is the most prevalent of the species causing HLB. The Asian citrus psyllid (ACP, Diaphorina citri) transmits Las. HLB is threatening citrus production worldwide, and there is no cure for infected trees. Management strategies targeting diseased trees at different stages of colonization by Las are needed for sustainable citrus production in HLB-endemic regions. We evaluated the effect of the combinations of plant defense elicitors, nitrogen (N) fertilizer, and compost on mildly diseased trees. We tested thermotherapy on severely diseased trees and assessed tree protectors to prevent feeding by ACP, thus preventing Las from being transmitted to new plantings that replaced HLB-moribund trees. After four applications over two consecutive growing seasons we found that the combination of compost, urea, and plant defense elicitors ß-aminobutyric acid, plus ascorbic acid and potassium phosphite with or without salicylic acid, slowed down the progression of HLB and reduced disease severity by approximately 18%, compared with the untreated control. Our data showed no decline in fruit yield, indeed treatment resulted in a higher yield compared with the untreated control. Thermotherapy treatment (55°C for 2 min) exhibited a suppressive effect on growth of Las and progress of HLB in severely diseased trees for 2 to 3 months after treatment. The tree protectors prevented feeding by ACP, and therefore young replant trees remained healthy and free from infection by Las over the 2-year duration of the experiment. Taken together, these results may contribute to a basis for developing a targeted approach to control HLB based on stage of host colonization, application of plant defense elicitors, N fertilizer, compost, thermotherapy, and tree protectors. There is potential to implement these strategies in conjunction with other disease control measures to contribute to sustainable citrus production in HLB-endemic regions.

Analysis and Evaluation of the Image Preprocessing Process of a Six-Band Multispectral Camera Mounted on an Unmanned Aerial Vehicle for Winter Wheat Monitoring.

Unmanned aerial vehicle (UAV)-based multispectral sensors have great potential in crop monitoring due to their high flexibility, high spatial resolution, and ease of operation. Image preprocessing, however, is a prerequisite to make full use of the acquired high-quality data in practical applications. Most crop monitoring studies have focused on specific procedures or applications, and there has been little attempt to examine the accuracy of the data preprocessing steps. This study focuses on the preprocessing process of a six-band multispectral camera (Mini-MCA6) mounted on UAVs. First, we have quantified and analyzed the components of sensor error, including noise, vignetting, and lens distortion. Next, different methods of spectral band registration and radiometric correction were evaluated. Then, an appropriate image preprocessing process was proposed. Finally, the applicability and potential for crop monitoring were assessed in terms of accuracy by measurement of the leaf area index (LAI) and the leaf biomass inversion under variable growth conditions during five critical growth stages of winter wheat. The results show that noise and vignetting could be effectively removed via use of correction coefficients in image processing. The widely used Brown model was suitable for lens distortion correction of a Mini-MCA6. Band registration based on ground control points (GCPs) (Root-Mean-Square Error, RMSE = 1.02 pixels) was superior to that using PixelWrench2 (PW2) software (RMSE = 1.82 pixels). For radiometric correction, the accuracy of the empirical linear correction (ELC) method was significantly higher than that of light intensity sensor correction (ILSC) method. The multispectral images that were processed using optimal correction methods were demonstrated to be reliable for estimating LAI and leaf biomass. This study provides a feasible and semi-automatic image preprocessing process for a UAV-based Mini-MCA6, which also serves as a reference for other array-type multispectral sensors. Moreover, the high-quality data generated in this study may stimulate increased interest in remote high-efficiency monitoring of crop growth status.

A Comprehensive Review on Food Applications of Terahertz Spectroscopy and Imaging.

Food product safety is a public health concern. Most of the food safety analytical and detection methods are expensive, labor intensive, and time consuming. A safe, rapid, reliable, and nondestructive detection method is needed to assure consumers that food products are safe to consume. Terahertz (THz) radiation, which has properties of both microwave and infrared, can penetrate and interact with many commonly used materials. Owing to the technological developments in sources and detectors, THz spectroscopic imaging has transitioned from a laboratory-scale technique into a versatile imaging tool with many practical applications. In recent years, THz imaging has been shown to have great potential as an emerging nondestructive tool for food inspection. THz spectroscopy provides qualitative and quantitative information about food samples. The main applications of THz in food industries include detection of moisture, foreign bodies, inspection, and quality control. Other applications of THz technology in the food industry include detection of harmful compounds, antibiotics, and microorganisms. THz spectroscopy is a great tool for characterization of carbohydrates, amino acids, fatty acids, and vitamins. Despite its potential applications, THz technology has some limitations, such as limited penetration, scattering effect, limited sensitivity, and low limit of detection. THz technology is still expensive, and there is no available THz database library for food compounds. The scanning speed needs to be improved in the future generations of THz systems. Although many technological aspects need to be improved, THz technology has already been established in the food industry as a powerful tool with great detection and quantification ability. This paper reviews various applications of THz spectroscopy and imaging in the food industry.

Detection of Huanglongbing disease using differential mobility spectrometry.

The viability of the multibillion dollar global citrus industry is threatened by the "green menace", citrus greening disease (Huanglongbing, HLB), caused by the bacterial pathogen Candidatus Liberibacter. The long asymptomatic stage of HLB makes it challenging to detect emerging regional infections early to limit disease spread. We have established a novel method of disease detection based on chemical analysis of released volatile organic compounds (VOCs) that emanate from infected trees. We found that the biomarkers "fingerprint" is specific to the causal pathogen and could be interpreted using analytical methods such as gas chromatography/mass spectrometry (GC/MS) and gas chromatography/differential mobility spectrometry (GC/DMS). This VOC-based disease detection method has a high accuracy of ∼90% throughout the year, approaching 100% under optimal testing conditions, even at very early stages of infection where other methods are not adequate. Detecting early infection based on VOCs precedes visual symptoms and DNA-based detection techniques (real-time polymerase chain reaction, RT-PCR) and can be performed at a substantially lower cost and with rapid field deployment.

Huanglongbing (citrus greening) detection using visible, near infrared and thermal imaging techniques.

This study demonstrates the applicability of visible-near infrared and thermal imaging for detection of Huanglongbing (HLB) disease in citrus trees. Visible-near infrared (440-900 nm) and thermal infrared spectral reflectance data were collected from individual healthy and HLB-infected trees. Data analysis revealed that the average reflectance values of the healthy trees in the visible region were lower than those in the near infrared region, while the opposite was the case for HLB-infected trees. Moreover, 560 nm, 710 nm, and thermal band showed maximum class separability between healthy and HLB-infected groups among the evaluated visible-infrared bands. Similarly, analysis of several vegetation indices indicated that the normalized difference vegetation index (NDVI), Vogelmann red-edge index (VOG) and modified red-edge simple ratio (mSR) demonstrated good class separability between the two groups. Classification studies using average spectral reflectance values from the visible, near infrared, and thermal bands (13 spectral features) as input features indicated that an average overall classification accuracy of about 87%, with 89% specificity and 85% sensitivity could be achieved with classification models such as support vector machine for trees with symptomatic leaves.

Evaluation of Visible-Near Infrared Reflectance Spectra of Avocado Leaves as a Non-destructive Sensing Tool for Detection of Laurel Wilt.

Laurel wilt, caused by the fungus Raffaelea lauricola, affects the growth, development, and productivity of avocado, Persea americana. This study evaluated the potential of visible-near infrared spectroscopy for non-destructive sensing of this disease. The symptoms of laurel wilt are visually similar to those caused by freeze damage (leaf necrosis). In this work, we performed classification studies with visible-near infrared spectra of asymptomatic and symptomatic leaves from infected plants, as well as leaves from freeze-damaged and healthy plants, both of which were non-infected. The principal component scores computed from principal component analysis were used as input features in four classifiers: linear discriminant analysis, quadratic discriminant analysis (QDA), Naïve-Bayes classifier, and bagged decision trees (BDT). Among the classifiers, QDA and BDT resulted in classification accuracies of higher than 94% when classifying asymptomatic leaves from infected plants. All of the classifiers were able to discriminate symptomatic-infected leaves from freeze-damaged leaves. However, the false negatives mainly resulted from asymptomatic-infected leaves being classified as healthy. Analyses of average vegetation indices of freeze-damaged, healthy (non-infected), asymptomatic-infected, and symptomatic-infected leaves indicated that the normalized difference vegetation index and the simple ratio index were statistically different.

SARS-CoV-2 pseudotyped virus persists on the surface of multiple produce but can be inactivated with gaseous ozone.

Due to the immense societal and economic impact that the COVID-19 pandemic has caused, limiting the spread of SARS-CoV-2 is one of the most important priorities at this time. The global interconnectedness of the food industry makes it one of the biggest concerns for SARS-CoV-2 outbreaks. Although fomites are currently considered a low-risk route of transmission for SARS-CoV-2, new variants of the virus can potentially alter the transmission dynamics. In this study, we compared the survival rate of pseudotyped SARS-CoV-2 on plastic with some commonly used food samples (i.e., apple, strawberry, grapes, tomato, cucumber, lettuce, parsley, Brazil nut, almond, cashew, and hazelnut). The porosity level and the chemical composition of different food products affect the virus's stability and infectivity. Our results showed that tomato, cucumber, and apple offer a higher survival rate for the pseudotyped viruses. Next, we explored the effectiveness of ozone in deactivating the SARS-CoV-2 pseudotyped virus on the surface of tomato, cucumber, and apple. We found that the virus was effectively inactivated after being exposed to 15 ppm of ozone for 1 h under ambient conditions. SEM imaging revealed that while ozone exposure altered the wax layer on the surface of produce, it did not seem to damage the cells and their biological structures. The results of our study indicate that ozonated air can likely provide a convenient method of effectively disinfecting bulk food shipments that may harbour the SARS-CoV-2 virus.

Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor.

Several diseases have threatened tomato production in Florida, resulting in large losses, especially in fresh markets. In this study, a high-resolution portable spectral sensor was used to investigate the feasibility of detecting multi-diseased tomato leaves in different stages, including early or asymptomatic stages. One healthy leaf and three diseased tomato leaves (late blight, target and bacterial spots) were defined into four stages (healthy, asymptomatic, early stage and late stage) and collected from a field. Fifty-seven spectral vegetation indices (SVIs) were calculated in accordance with methods published in previous studies and established in this study. Principal component analysis was conducted to evaluate SVIs. Results revealed six principal components (PCs) whose eigenvalues were greater than 1. SVIs with weight coefficients ranking from 1 to 30 in each selected PC were applied to a K-nearest neighbour for classification. Amongst the examined leaves, the healthy ones had the highest accuracy (100%) and the lowest error rate (0) because of their uniform tissues. Late stage leaves could be distinguished more easily than the two other disease categories caused by similar symptoms on the multi-diseased leaves. Further work may incorporate the proposed technique into an image system that can be operated to monitor multi-diseased tomato plants in fields.

Detection of Anomalies in Citrus Leaves Using Laser-Induced Breakdown Spectroscopy (LIBS).

Nutrient assessment and management are important to maintain productivity in citrus orchards. In this study, laser-induced breakdown spectroscopy (LIBS) was applied for rapid and real-time detection of citrus anomalies. Laser-induced breakdown spectroscopy spectra were collected from citrus leaves with anomalies such as diseases (Huanglongbing, citrus canker) and nutrient deficiencies (iron, manganese, magnesium, zinc), and compared with those of healthy leaves. Baseline correction, wavelet multivariate denoising, and normalization techniques were applied to the LIBS spectra before analysis. After spectral pre-processing, features were extracted using principal component analysis and classified using two models, quadratic discriminant analysis and support vector machine (SVM). The SVM resulted in a high average classification accuracy of 97.5%, with high average canker classification accuracy (96.5%). LIBS peak analysis indicated that high intensities at 229.7, 247.9, 280.3, 393.5, 397.0, and 769.8 nm were observed of 11 peaks found in all the samples. Future studies using controlled experiments with variable nutrient applications are required for quantification of foliar nutrients by using LIBS-based sensing.

Detection of laurel wilt disease in avocado using low altitude aerial imaging.

Laurel wilt is a lethal disease of plants in the Lauraceae plant family, including avocado (Persea americana). This devastating disease has spread rapidly along the southeastern seaboard of the United States and has begun to affect commercial avocado production in Florida. The main objective of this study was to evaluate the potential to discriminate laurel wilt-affected avocado trees using aerial images taken with a modified camera during helicopter surveys at low-altitude in the commercial avocado production area. The ability to distinguish laurel wilt-affected trees from other factors that produce similar external symptoms was also studied. RmodGB digital values of healthy trees and laurel wilt-affected trees, as well as fruit stress and vines covering trees were used to calculate several vegetation indices (VIs), band ratios, and VI combinations. These indices were subjected to analysis of variance (ANOVA) and an M-statistic was performed in order to quantify the separability of those classes. Significant differences in spectral values among laurel wilt affected and healthy trees were observed in all vegetation indices calculated, although the best results were achieved with Excess Red (ExR), (Red-Green) and Combination 1 (COMB1) in all locations. B/G showed a very good potential for separate the other factors with symptoms similar to laurel wilt-affected trees, such as fruit stress and vines covering trees, from laurel wilt-affected trees. These consistent results prove the usefulness of using a modified camera (RmodGB) to discriminate laurel wilt-affected avocado trees from healthy trees, as well as from other factors that cause the same symptoms and suggest performing the classification in further research. According to our results, ExR and B/G should be utilized to develop an algorithm or decision rules to classify aerial images, since they showed the highest capacity to discriminate laurel wilt-affected trees. This methodology may allow the rapid detection of laurel wilt-affected trees using low altitude aerial images and be a valuable tool in mitigating this important threat to Florida avocado production.

Detection of Huanglongbing-infected citrus leaves using statistical models with a fluorescence sensor.

A handheld fluorescence sensor was tested as a sensing tool to identify Huanglongbing (HLB), a citrus disease, in both symptomatic and asymptomatic stages. Features such as yellow, red, and far-red fluorescence at UV, blue, green, and red excitations, and other fluorescence ratios were acquired from the healthy and HLB-infected leaves of different cultivars. The classification studies were performed with these features as well as selective fluorescence features. Results indicated that the bagged decision tree classifier yielded 97% classification accuracy in case of the healthy and symptomatic samples. Although the asymptomatic samples from the HLB-infected trees could not be classified based on polymerase chain reaction (PCR) analysis results, the Naïve-Bayes classifier grouped most of the asymptomatic samples as HLB. We found that a few fluorescence features such as yellow fluorescence (UV), far-red fluorescence (UV), yellow to far red fluorescence (UV), simple fluorescence ratio (green), and yellow fluorescence (green) could result in classification accuracies similar to those of the entire dataset.

Mid-infrared spectroscopy for detection of Huanglongbing (greening) in citrus leaves.

In recent years, Huanglongbing (HLB) also known as citrus greening has greatly affected citrus orchards in Florida. This disease has caused significant economic and production losses costing about $750/acre for HLB management. Early and accurate detection of HLB is a critical management step to control the spread of this disease. This work focuses on the application of mid-infrared spectroscopy for the detection of HLB in citrus leaves. Leaf samples of healthy, nutrient-deficient, and HLB-infected trees were processed in two ways (process-1 and process-2) and analyzed using a rugged, portable mid-infrared spectrometer. Spectral absorbance data from the range of 5.15-10.72 µm (1942-933 cm(-1)) were preprocessed (baseline correction, negative offset correction, and removal of water absorbance band) and used for data analysis. The first and second derivatives were calculated using the Savitzky-Golay method. The preprocessed raw dataset, first derivatives dataset, and second derivatives dataset were first analyzed by principal component analysis. Then, the selected principal component scores were classified using two classification algorithms, quadratic discriminant analysis (QDA) and k-nearest neighbor (kNN). When the spectral data from leaf samples processed using process-1 were used for data analysis, the kNN-based algorithm yielded higher classification accuracies (especially nutrient-deficient leaf class) than that of the other spectral data (process-2). The performance of the kNN-based algorithm (higher than 95%) was better than the QDA-based algorithm. Moreover, among different types of datasets, preprocessed raw dataset resulted in higher classification accuracies than first and second derivatives datasets. The spectral peak in the region of 9.0-10.5 µm (952-1112 cm(-1)) was found to be distinctly different between the healthy and HLB-infected leaf samples. This carbohydrate peak could be attributed to the starch accumulation in the HLB-infected citrus leaves. Thus, this study demonstrates the applicability of mid-infrared spectroscopy for HLB detection in citrus.