Automatic Localization of Soybean Seedlings Based on Crop Signaling and Multi-View Imaging.

Soybean is grown worldwide for its high protein and oil content. Weeds compete fiercely for resources, which affects soybean yields. Because of the progressive enhancement of weed resistance to herbicides and the quickly increasing cost of manual weeding, mechanical weed control is becoming the preferred method of weed control. Mechanical weed control finds it difficult to remove intra-row weeds due to the lack of rapid and precise weed/soybean detection and location technology. Rhodamine B (Rh-B) is a systemic crop compound that can be absorbed by soybeans which fluoresces under a specific excitation light. The purpose of this study is to combine systemic crop compounds and computer vision technology for the identification and localization of soybeans in the field. The fluorescence distribution properties of systemic crop compounds in soybeans and their effects on plant growth were explored. The fluorescence was mainly concentrated in soybean cotyledons treated with Rh-B. After a comparison of soybean seedlings treated with nine groups of rhodamine B solutions at different concentrations ranging from 0 to 1440 ppm, the soybeans treated with 180 ppm Rh-B for 24 h received the recommended dosage, resulting in significant fluorescence that did not affect crop growth. Increasing the Rh-B solutions reduced crop biomass, while prolonged treatment times reduced seed germination. The fluorescence produced lasted for 20 days, ensuring a stable signal in the early stages of growth. Additionally, a precise inter-row soybean plant location system based on a fluorescence imaging system with a 96.7% identification accuracy, determined on 300 datasets, was proposed. This article further confirms the potential of crop signaling technology to assist machines in achieving crop identification and localization in the field.

A real-time smart sensing system for automatic localization and recognition of vegetable plants for weed control.

Tomato is a globally grown vegetable crop with high economic and nutritional values. Tomato production is being threatened by weeds. This effect is more pronounced in the early stages of tomato plant growth. Thus weed management in the early stages of tomato plant growth is very critical. The increasing labor cost of manual weeding and the negative impact on human health and the environment caused by the overuse of herbicides are driving the development of smart weeders. The core task that needs to be addressed in developing a smart weeder is to accurately distinguish vegetable crops from weeds in real time. In this study, a new approach is proposed to locate tomato and pakchoi plants in real time based on an integrated sensing system consisting of camera and color mark sensors. The selection scheme of reference, color, area, and category of plant labels for sensor identification was examined. The impact of the number of sensors and the size of the signal tolerance region on the system recognition accuracy was also evaluated. The experimental results demonstrated that the color mark sensor using the main stem of tomato as the reference exhibited higher performance than that of pakchoi in identifying the plant labels. The scheme of applying white topical markers on the lower main stem of the tomato plant is optimal. The effectiveness of the six sensors used by the system to detect plant labels was demonstrated. The computer vision algorithm proposed in this study was specially developed for the sensing system, yielding the highest overall accuracy of 95.19% for tomato and pakchoi localization. The proposed sensor-based system is highly accurate and reliable for automatic localization of vegetable plants for weed control in real time.

Discrimination of Deoxynivalenol Levels of Barley Kernels Using Hyperspectral Imaging in Tandem with Optimized Convolutional Neural Network.

Deoxynivalenol (DON) in raw and processed grain poses significant risks to human and animal health. In this study, the feasibility of classifying DON levels in different genetic lines of barley kernels was evaluated using hyperspectral imaging (HSI) (382-1030 nm) in tandem with an optimized convolutional neural network (CNN). Machine learning methods including logistic regression, support vector machine, stochastic gradient descent, K nearest neighbors, random forest, and CNN were respectively used to develop the classification models. Spectral preprocessing methods including wavelet transform and max-min normalization helped to enhance the performance of different models. A simplified CNN model showed better performance than other machine learning models. Competitive adaptive reweighted sampling (CARS) in combination with successive projections algorithm (SPA) was applied to select the best set of characteristic wavelengths. Based on seven wavelengths selected, the optimized CARS-SPA-CNN model distinguished barley grains with low levels of DON (<5 mg/kg) from those with higher levels (5 mg/kg < DON ≤ 14 mg/kg) with an accuracy of 89.41%. The lower levels of DON class I (0.19 mg/kg ≤ DON ≤ 1.25 mg/kg) and class II (1.25 mg/kg < DON ≤ 5 mg/kg) were successfully distinguished based on the optimized CNN model, yielding a precision of 89.81%. The results suggest that HSI in tandem with CNN has great potential for discrimination of DON levels of barley kernels.

YOLOV5-CBAM-C3TR: an optimized model based on transformer module and attention mechanism for apple leaf disease detection.

Apple trees face various challenges during cultivation. Apple leaves, as the key part of the apple tree for photosynthesis, occupy most of the area of the tree. Diseases of the leaves can hinder the healthy growth of trees and cause huge economic losses to fruit growers. The prerequisite for precise control of apple leaf diseases is the timely and accurate detection of different diseases on apple leaves. Traditional methods relying on manual detection have problems such as limited accuracy and slow speed. In this study, both the attention mechanism and the module containing the transformer encoder were innovatively introduced into YOLOV5, resulting in YOLOV5-CBAM-C3TR for apple leaf disease detection. The datasets used in this experiment were uniformly RGB images. To better evaluate the effectiveness of YOLOV5-CBAM-C3TR, the model was compared with different target detection models such as SSD, YOLOV3, YOLOV4, and YOLOV5. The results showed that YOLOV5-CBAM-C3TR achieved mAP@0.5, precision, and recall of 73.4%, 70.9%, and 69.5% for three apple leaf diseases including Alternaria blotch, Grey spot, and Rust. Compared with the original model YOLOV5, the mAP 0.5increased by 8.25% with a small change in the number of parameters. In addition, YOLOV5-CBAM-C3TR can achieve an average accuracy of 92.4% in detecting 208 randomly selected apple leaf disease samples. Notably, YOLOV5-CBAM-C3TR achieved 93.1% and 89.6% accuracy in detecting two very similar diseases including Alternaria Blotch and Grey Spot, respectively. The YOLOV5-CBAM-C3TR model proposed in this paper has been applied to the detection of apple leaf diseases for the first time, and also showed strong recognition ability in identifying similar diseases, which is expected to promote the further development of disease detection technology.

Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model.

Precise segmentation of wheat spikes from a complex background is necessary for obtaining image-based phenotypic information of wheat traits such as yield estimation and spike morphology. A new instance segmentation method based on a Hybrid Task Cascade model was proposed to solve the wheat spike detection problem with improved detection results. In this study, wheat images were collected from fields where the environment varied both spatially and temporally. Res2Net50 was adopted as a backbone network, combined with multi-scale training, deformable convolutional networks, and Generic ROI Extractor for rich feature learning. The proposed methods were trained and validated, and the average precision (AP) obtained for the bounding box and mask was 0.904 and 0.907, respectively, and the accuracy for wheat spike counting was 99.29%. Comprehensive empirical analyses revealed that our method (Wheat-Net) performed well on challenging field-based datasets with mixed qualities, particularly those with various backgrounds and wheat spike adjacence/occlusion. These results provide evidence for dense wheat spike detection capabilities with masking, which is useful for not only wheat yield estimation but also spike morphology assessments.

Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review.

Fluorescence spectroscopy, color imaging and multispectral imaging (MSI) have emerged as effective analytical methods for the non-destructive detection of quality attributes of various white meat products such as fish, shrimp, chicken, duck and goose. Based on machine learning and convolutional neural network, these techniques can not only be used to determine the freshness and category of white meat through imaging and analysis, but can also be used to detect various harmful substances in meat products to prevent stale and spoiled meat from entering the market and causing harm to consumer health and even the ecosystem. The development of quality inspection systems based on such techniques to measure and classify white meat quality parameters will help improve the productivity and economic efficiency of the meat industry, as well as the health of consumers. Herein, a comprehensive review and discussion of the literature on fluorescence spectroscopy, color imaging and MSI is presented. The principles of these three techniques, the quality analysis models selected and the research results of non-destructive determinations of white meat quality over the last decade or so are analyzed and summarized. The review is conducted in this highly practical research field in order to provide information for future research directions. The conclusions detail how these efficient and convenient imaging and analytical techniques can be used for non-destructive quality evaluation of white meat in the laboratory and in industry.

Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality.

Imaging spectroscopy has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of agricultural products. By providing spectral information relevant to food quality properties, imaging spectroscopy has been demonstrated to be a potential method for rapid and non-destructive classification, authentication, and prediction of quality parameters of various categories of tubers, including potato and sweet potato. The imaging technique has demonstrated great capacities for gaining rapid information about tuber physical properties (such as texture, water binding capacity, and specific gravity), chemical components (such as protein, starch, and total anthocyanin), varietal authentication, and defect aspects. This paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers. The machine learning algorithms coupled with feature variable identification approaches have obtained acceptable results. This review briefly introduces imaging spectroscopy and machine learning, then provides examples and discussions of these techniques in tuber quality determinations, and presents the challenges and future prospects of the technology. This review will be of great significance to the study of tubers using spectral imaging technology.

Hyperspectral imaging and improved feature variable selection for automated determination of deoxynivalenol in various genetic lines of barley kernels for resistance screening.

Fusarium head blight (FHB), a fungus disease of small grain cereal crops, results in reduced yields and diminished value of harvested grain due to the presence of deoxynivalenol (DON), a mycotoxin produced by the causal pathogen Fusarium graminearum. DON and other tricothecene mycotoxins pose serious health risks to both humans and livestock, especially swine. Due to these health concerns, barley used for malting, food or feed is routinely assayed for DON levels. Various methods are available for assaying DON levels in grain samples including enzyme-linked immunosorbent assay (ELISA) and gas chromatography-mass spectrometry (GC-MS). ELISA and GC-MS are very accurate; however, assaying grain samples by these techniques are laborious, expensive and destructive. In this study, we explored the feasibility of using hyperspectral imaging (382-1030 nm) to develop a rapid and non-destructive protocol for assaying DON in barley kernels. Samples of 888 and 116 from various genetic lines were selected for calibration and prediction. Full-wavelength locally weighted partial least squares regression (LWPLSR) achieved high accuracy with the coefficient of determination in prediction (R2P) of 0.728 and root mean square error of prediction (RMSEP) of 3.802. Competitive adaptive reweighted sampling (CARS) was used to choose potential feature wavelengths, and these selected variables were further optimized using the iterative selection of successive projections algorithm (ISSPA). The CARS-ISSPA-LWPLSR model developed using 7 feature variables yielded R2P of 0.680 and RMSEP of 4.213 in DON content prediction. Based on the 7 wavelengths selected by CARS-ISSPA, partial least square discriminant analysis (PLSDA) discriminated barley kernels having lower DON (less than1.25 mg/kg) levels from those with higher levels (including 1.25-3 mg/kg, 3-5 mg/kg, and 5-10 mg/kg), with Matthews correlation coefficient in cross-validation (M-RCV) of as high as 0.931. The results demonstrate that hyperspectral imaging have potential for accelerating non-destructive DON assays of barley samples.

Advanced Analysis of Roots and Tubers by Hyperspectral Techniques.

Hyperspectral techniques in terms of spectroscopy and hyperspectral imaging have become reliable analytical tools to effectively describe quality attributes of roots and tubers (such as potato, sweet potato, cassava, yam, taro, and sugar beet). In addition to the ability for obtaining rapid information about food external or internal defects including sprout, bruise, and hollow heart, and identifying different grades of food quality, such techniques have also been implemented to determine physical properties (such as color, texture, and specific gravity) and chemical constituents (such as protein, vitamins, and carotenoids) in root and tuber products with avoidance of extensive sample preparation. Developments of related quality evaluation systems based on hyperspectral data that determine food quality parameters would bring about economic and technical values to the food industry. Consequently, a comprehensive review of hyperspectral literature is carried out in this chapter. The spectral data acquired, the multivariate statistical methods used, and the main breakthroughs of recent studies on quality determinations of root and tuber products are discussed and summarized. The conclusion elaborates the promise of how hyperspectral techniques can be applied for non-invasive and rapid evaluations of tuber quality properties.

Nicotine modulates the facial stimulation-evoked responses in cerebellar granule cell layer in vivo in mice.

Nicotinic acetylcholine receptors are cationic channels that mediate fast excitatory transmission in the central nervous system. Several nicotinic acetylcholine receptor subunits have been detected within cerebellar granule cell layer (GCL), and activation of these receptors may have a significant influence on neuronal synaptic transmission of the cerebellum. The aim of present study was to better understand the roles of nicotinic acetylcholine receptors during the sensory stimulation-evoked synaptic transmission in the cerebellar GCL. Our results showed that cerebellar surface perfusion of nicotine significantly facilitated the cerebellar GCL field potential responses evoked by air-puff stimulation of ipsilateral whisker pad, which exhibited increases in amplitude and area under the curve (AUC) of both stimulus onset responses (N1) and stimulus offset responses (N2). The nicotine-induced increase in AUC of facial stimulation-evoked N1 was dose-dependent with a 50% effective concentration (EC50) of 32.6 µM. Application of either a selective α4ß2 nicotinic acetylcholine receptors antagonist, DHßE (1 µM) or a selective α7 nicotinic acetylcholine receptors antagonist, MLA (1 µM) alone attenuated, but not completely abolished the nicotine-induced increases in the amplitude and AUC of the facial stimulation-evoked N1. However, simultaneous blockade of α7 and α4ß2 nicotinic acetylcholine receptor subunits abolished the nicotine-induced increase in the amplitude of N1. These results indicate that nicotine activates α7 and α4ß2 nicotinic acetylcholine receptor subunits, resulting in an enhancement of facial stimulation-evoked responses in mouse cerebellar GCL. Our results suggest that nicotine modulates the sensory information processing in the cerebellar GCL through α7 and α4ß2 subunits nicotinic acetylcholine receptors.

Proportional dose of rapid-onset opioid in breakthrough cancer pain management: An open-label, multicenter study.

BACKGROUND: The management of breakthrough pain (BTP) in cancer patients is a challenge. It is clinically useful to evaluate the effectiveness of rapid-onset opioid at a starting dose in proportional to the background opioid regimen. This open-label, multicenter, noncomparative study aimed to assess the efficacy and safety of proportional doses of fentanyl buccal soluble film (FBSF) in patients with breakthrough cancer pain. METHODS: Thirty patients aged 20 to 70, experiencing 1 to 3 BTP per day, receiving regimens equivalent to 60 to 360 mg/day of oral morphine or 25 to 150 µg/h of transdermal fentanyl ≥1 week, were prospectively recruited. FBSF was administered proportionally based on their current opioid regimen for baseline pain. The percentage of patients requiring dose titration was evaluated. For each BTP episode, changes in pain intensity at 30 minutes (PID30) after dosing, patient's satisfaction, the percentage of episodes requiring rescue medication, and adverse events (AEs) were recorded. RESULTS: The percentage of patients who required dose titration was 21.4% (6/28) and 12.0% (3/25) in the full analysis set and per-protocol populations, respectively. The average PID30 was 3.9, and a pain score ≤3 was achieved in 95.1% of the events. Eight out of 367 (2.2%) BTP episodes needed rescue medication. The majority of subjects (75.8%) rated their experience of pain management as good to excellent. A total of 6 drug-related AEs were reported by 3 (10.7%) patients in the safety population. CONCLUSIONS: FBSF dose in proportional to the regimen of opioid for baseline pain management is efficacious and well tolerated for the treatment of cancer patients with BTP.

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review.

Fourier transform infrared (FT-IR) and Raman and hyperspectral imaging (HSI) techniques have emerged as reliable analytical methods for effectively characterizing and quantifying quality attributes of different categories of powdery food products (such as milk powder, tea powder, cocoa powder, coffee powder, soybean flour, wheat flour, and chili powder). In addition to the ability for gaining rapid information about food chemical components (such as moisture, protein, and starch), and classifying food quality into different grades, such techniques have also been implemented to determine trace impurities in pure foods and other properties of particulate foods and ingredients with avoidance of extensive sample preparation. Developments of corresponding quality evaluation systems based on FT-IR, Raman, and HSI data that measure food quality parameters and ensure product authentication, would bring about technical and economic benefits to the food industry by enhancing consumer confidence in the quality of its products. Accordingly, a comprehensive review of the mushrooming spectroscopy-based FT-IR, Raman, and HSI literature is carried out in this article. The spectral data collected, the chemometric methods used, and the main findings of recent research studies on quality assessments of powdered materials are discussed and summarized. Providing a review in such a flourishing research field is relevant as a signpost for future study. The conclusion details the promise of how such noninvasive and powerful analytical techniques can be used for rapid and accurate determinations of powder quality attributes in both academical and industrial settings.

Multispectral Imaging for Plant Food Quality Analysis and Visualization.

The multispectral imaging technique is considered a reformation of hyperspectral imaging. It can be employed to noninvasively and rapidly evaluate food quality. Even though several imaging or sensor-based techniques have been conducted for the quality assessment of various food products, the rise of multispectral imaging has been more promising. This paper presents a comprehensive review of the use of the multispectral sensor in the quality assessment of plant foods (such as cereals, legumes, tubers, fruits, and vegetables). Different quality parameters (such as physicochemical and microbiological aspects) of plant-based foods that were determined and visualized by the combination of modeling methods and feature wavelength selection approaches are summarized. Based on the literature, the most frequently used wavelength selection methods are the successive projection algorithm (SPA) and the regression coefficient (RC). The most effective models developed for analyzing plant food products are the partial least squares regression (PLSR), least square support vector machine (LS-SVM), support vector machine (SVM), partial least squares discriminant analysis (PLSDA), and multiple linear regression (MLR). This article concludes with a discussion of challenges, potential uses, and future trends of this flourishing technique that is now also being applied to plant foods.

Non-Destructive and rapid evaluation of staple foods quality by using spectroscopic techniques: A review.

Staple foods, including cereals, legumes, and root/tuber crops, dominate the daily diet of humans by providing valuable proteins, starch, oils, minerals, and vitamins. Quality evaluation of staple foods is primarily carried out on sensory (e.g. external defect, color), adulteration (e.g. species, origin), chemical (e.g. starch, proteins), mycotoxin (e.g. Fusarium toxin, aflatoxin), parasitic infection (e.g. weevil, beetle), and internal physiological (e.g. hollow heart, black heart) aspects. Conventional methods for the quality assessment of staple foods are always laborious, destructive, and time-consuming. Requirements for online monitoring of staple foods have been proposed to encourage the development of rapid, reagentless, and noninvasive techniques. Spectroscopic techniques, such as visible-infrared spectroscopy, Raman spectroscopy, nuclear magnetic resonance spectroscopy, and spectral imaging, have been introduced as promising analytical tools and applied for the quality evaluation of staple foods. This review summarizes the recent applications and progress of such spectroscopic techniques in determining various qualities of staple foods. Besides, challenges and future trends of these spectroscopic techniques are also presented.

Facilitated wavelength selection and model development for rapid determination of the purity of organic spelt (Triticum spelta L.) flour using spectral imaging.

Based on a new approach for wavelength selection, a multispectral real-time imaging system was proposed for the staple food industry to determine the fidelity of organic spelt flour (OSF) from three categories of adulterants including rye flour (RF), organic wheat flour (OWF) and spelt flour (SF). Calibration models were first built by partial least squares discriminant analysis (PLSDA) and partial least squares regression (PLSR) with spectral pretreatment for multivariate analysis of hyperspectral image in the spectral range of 900-1700nm. Instead of qualifying certain groups of characteristic wavelengths for RF, OWF, SF and OSF separately, a set of mutual wavelengths (1145, 1192, 1222, 1349, 1359, 1396, 1541, and 1567nm) was chosen by first-derivative and mean centring iteration algorithm (FMCIA) for all investigated flour samples. Then these selected feature wavelengths were utilized in PLSDA, PLSR and multiple linear regression (MLR) models to devise multispectral imaging system. Better performances for both qualitative discrimination of OSF and quantitative measure of adulterants were emerged in simplified PLSDA and PLSR models, with mean determination coefficients in cross validation (R(2)CV) of 0.958 and in prediction (R(2)P) of 0.957, respectively. To visualize the adulterants in OSF samples, the distribution maps were drawn by computing the spectral response of each pixel on corresponding spectral images at specific frequencies using a quantitative identification function. The results reveal that spectral imaging integrated with multivariate analysis has good potential for rapidly evaluating the purity of organic spelt flour.

A novel infusible botanically-derived drug, PG2, for cancer-related fatigue: a phase II double-blind, randomized placebo-controlled study.

PURPOSE: This study investigated the efficacy of the botanical-derived drug, PG2, a partially purified extract of Astragalus membranaceus, as a complementary and palliative medicine for managing cancer-related fatigue (CRF). METHODS: Patients with advanced cancer and moderate to severe CRF were randomized to receive either PG2 or a placebo (normal saline, NS) in the first treatment cycle (four weeks) in a double-blind manner; thereafter, on the next cycle (four weeks), all patients received open-label treatment with PG2. RESULTS: PG2 significantly improved CRF in the NS-primed group. In the first four week cycle, PG2 administration resulted in a greater fatigue-improvement response rate than seen with NS alone. In addition, approximately 82% of patients who reported an improvement of fatigue symptoms following the first cycle of PG2 experienced sustained benefits after administration of the second treatment cycle. Among patients treated with PG2 who did not report an improvement in symptoms throughout the first treatment cycle, approximately 71% showed significant improvement after the second treatment cycle. No major or irreversible toxicities were observed with PG2 treatment. CONCLUSION: PG2 might be an effective and safe treatment for relieving CRF among advanced cancer patients.

Application of quality audit tools to evaluate care quality received by terminal cancer patients admitted to a palliative care unit.

GOALS: We aimed to evaluate the palliative care quality for terminal cancer patients as a reference for prospective service improvements. PATIENTS AND METHODS: A prospective study was conducted to analyze the quality audit data collected from 1,476 samples in one medical center's palliative care unit. Lai's modified version of the support team assessment schedule was utilized for 2-year data collection, applying 17 categories of audit criteria rated on a scale of 0 to 4 (0-no symptoms, 4-significant symptoms). The quality audit criteria were scored weekly for 2 weeks following patient admission. RESULTS: Average scores of quality audit criteria from admission to week 2 were 10.05, 8.85, and 8.49, respectively, with statistical differences noted weekly. Efficacy of palliative care was most evident 2 weeks after hospitalization. Factors with statistically significant differences between admission and week 1 included: pain control, constipation, vomiting, anorexia, other symptom control, patient anxiety, self awareness and preparation of patient for illness, self awareness and preparation of family for illness, and satisfaction of patient's spiritual belief and life philosophy. Factors with statistically significant differences between weeks 1 and 2 included: pain control, other symptom control, self awareness and preparation of family for illness, and satisfaction of patient's spiritual belief and life philosophy. CONCLUSIONS: Patients in hospice care received adequate symptom relief. Total quality audit scores declined progressively over the 2 weeks following patient admission, indicating efficacy of palliative care quality.

Combined effect of topical arsenic trioxide and radiation therapy on skin-infiltrating lesions of breast cancer-a pilot study.

It has been reported that arsenic trioxide (As2O3) is an apoptosis inducer and radiation sensitizer for various cancer cell lines. In this study of breast cancer patients, we examined the combined effect of topical As2O3 and radiation therapy on fungating and/or skin-infiltrating lesions of breast cancer. The dermatological, gastrointestinal, hematological, renal and hepatic toxicities of the treatment were also monitored. As2O3 gel (0.05%) was applied to tumor lesions 1 h prior to delivery of each fraction, with the gel removed about 5 min before the irradiation. Superficial radiation was delivered using an electron beam from a linear accelerator. Every week, the tumor lesions were photographed to evaluate effectiveness, and blood was sampled to monitor changes in hemogram and biochemical profile. Seven breast cancer patients with cutaneous metastasis were enrolled in this study. In terms of tumor, the rates for complete, partial response and stable disease were 42.9 (three of seven), 42.9 (three of seven) and 14.3% (one of seven), respectively. The skin pain, assessed by a visual analog scale, and secretion from all of the seven superficial and fungating wounds decreased markedly after treatment. Significant bone marrow suppression or granulocytosis was not noted. Further, changes in renal and hepatic function were also not significant. It seems reasonable to conclude that As2O3 may be an effective and safe radiosensitizer for palliative radiotherapy for skin-infiltrating lesions of breast cancer.