Recent advances in E-monitoring of plant diseases

Determination of heavy metals in edible oils by a novel voltammetry taste sensor array

Herein, a novel voltammetry taste sensor array (VTSA) using pencil graphite electrode, screen-printed electrode, and glassy carbon electrode was used to identify heavy metals (HM) including Cad, Pb, Sn and Ni in soybean and rapeseed oils. HMs were added to edible oils at three concentrations of 0.05, 0.1 and 0.25 ppm, and then, the output of the device was classified using a chemometric classification method. According to the principal component analysis results, PG electrode explains 96% and 81% of the variance between the data in rapeseed and soybean edible oils, respectively. Additionally, the SP electrode explains 91% of the variance between the data in rapeseed and soybean oils. Moreover, the GC electrode explains 100% and 99% of the variance between the data in rapeseed and soybean edible oils, respectively. K-nearest neighbor exhibited high capability in classifying HMs in edible oils. In addition, partial least squares in the combine of VTSA shows a predict 99% in rapeseed oil. The best electrode for soybean edible oil was GC.

Factors affecting the production of sugarcane yield and sucrose accumulation: suggested potential biological solutions

Environmental stresses are the main constraints on agricultural productivity and food security worldwide. This issue is worsened by abrupt and severe changes in global climate. The formation of sugarcane yield and the accumulation of sucrose are significantly influenced by biotic and abiotic stresses. Understanding the biochemical, physiological, and environmental phenomena associated with these stresses is essential to increase crop production. This review explores the effect of environmental factors on sucrose content and sugarcane yield and highlights the negative effects of insufficient water supply, temperature fluctuations, insect pests, and diseases. This article also explains the mechanism of reactive oxygen species (ROS), the role of different metabolites under environmental stresses, and highlights the function of environmental stress-related resistance genes in sugarcane. This review further discusses sugarcane crop improvement approaches, with a focus on endophytic mechanism and consortium endophyte application in sugarcane plants. Endophytes are vital in plant defense; they produce bioactive molecules that act as biocontrol agents to enhance plant immune systems and modify environmental responses through interaction with plants. This review provides an overview of internal mechanisms to enhance sugarcane plant growth and environmental resistance and offers new ideas for improving sugarcane plant fitness and crop productivity.

A 3D-printed hollow microneedle-based electrochemical sensing device for in situ plant health monitoring

Plant health monitoring is devised as a new concept to elucidate in situ physiological processes. The need for increased food production to nourish the growing global population is inconsistent with the dramatic impact of climate change, which hinders crop health and exacerbates plant stress. In this context, wearable sensors play a crucial role in assessing plant stress. Herein, we present a low-cost 3D-printed hollow microneedle array (HMA) patch as a sampling device coupled with biosensors based on screen-printing technology, leading to affordable analysis of biomarkers in the plant fluid of a leaf. First, a refinement of the 3D-printing method showed a tip diameter of 25.9 ± 3.7 μm with a side hole diameter on the microneedle of 228.2 ± 18.6 μm using an affordable 3D printer (<500 EUR). Notably, the HMA patch withstanded the forces exerted by thumb pressing (i.e. 20-40 N). Subsequently, the holes of the HMA enabled the fluid extraction tested in vitro and in vivo in plant leaves (i.e. 13.5 ± 1.1 μL). A paper-based sampling strategy adapted to the HMA allowed the collection of plant fluid. Finally, integrating the sampling device onto biosensors facilitated the in situ electrochemical analysis of plant health biomarkers (i.e. H2O2, glucose, and pH) and the electrochemical profiling of plants in five plant species. Overall, this electrochemical platform advances precise and versatile sensors for plant health monitoring. The wearable device can potentially improve precision farming practices, addressing the critical need for sustainable and resilient agriculture in changing environmental conditions.

Extrusion Printing of Surface-Functionalized Metal-Organic Framework Inks for a High-Performance Wearable Volatile Organic Compound Sensor

Wearable sensors hold immense potential for real-time and non-destructive sensing of volatile organic compounds (VOCs), requiring both efficient sensing performance and robust mechanical properties. However, conventional colorimetric sensor arrays, acting as artificial olfactory systems for highly selective VOC profiling, often fail to meet these requirements simultaneously. Here, a high-performance wearable sensor array for VOC visual detection is proposed by extrusion printing of hybrid inks containing surface-functionalized sensing materials. Surface-modified hydrophobic polydimethylsiloxane (PDMS) improves the humidity resistance and VOC sensitivity of PDMS-coated dye/metal-organic frameworks (MOFs) composites. It also enhances their dispersion within liquid PDMS matrix, thereby promoting the hybrid liquid as high-quality extrusion-printing inks. The inks enable direct and precise printing on diverse substrates, forming a uniform and high particle-loading (70 wt%) film. The printed film on a flexible PDMS substrate demonstrates satisfactory flexibility and stretchability while retaining excellent sensing performance from dye/MOFs@PDMS particles. Further, the printed sensor array exhibits enhanced sensitivity to sub-ppm VOC levels, remarkable resistance to high relative humidity (RH) of 90%, and the differentiation ability for eight distinct VOCs. Finally, the wearable sensor proves practical by in situ monitoring of wheat scab-related VOC biomarkers. This study presents a versatile strategy for designing effective wearable gas sensors with widespread applications.

Integrated Management of Pathogens and Microbes in Cannabis sativa L. (Cannabis) under Greenhouse Conditions

The increased cultivation of high THC-containing Cannabis sativa L. (cannabis), particularly in greenhouses, has resulted in a greater incidence of diseases and molds that can negatively affect the growth and quality of the crop. Among them, the most important diseases are root rots (Fusarium and Pythium spp.), bud rot (Botrytis cinerea), powdery mildew (Golovinomyces ambrosiae), cannabis stunt disease (caused by hop latent viroid), and a range of microbes that reduce post-harvest quality. An integrated management approach to reduce the impact of these diseases/microbes requires combining different approaches that target the reproduction, spread, and survival of the associated pathogens, many of which can occur on the same plant simultaneously. These approaches will be discussed in the context of developing an integrated plan to manage the important pathogens of greenhouse-grown cannabis at different stages of plant development. These stages include the maintenance of stock plants, propagation through cuttings, vegetative growth of plants, and flowering. The cultivation of cannabis genotypes with tolerance or resistance to various pathogens is a very important approach, as well as the maintenance of pathogen-free stock plants. When combined with cultural approaches (sanitation, management of irrigation, and monitoring for diseases) and environmental approaches (greenhouse climate modification), a significant reduction in pathogen development and spread can be achieved. The use of preventive applications of microbial biological control agents and reduced-risk biorational products can also reduce disease development at all stages of production in jurisdictions where they are registered for use. The combined use of promising strategies for integrated disease management in cannabis plants during greenhouse production will be reviewed. Future areas for research are identified.

A Roadmap from Functional Materials to Plant Health Monitoring (PHM)

Soft bioelectronics have great potential for the early diagnosis of plant diseases and the mitigation of adverse outcomes such as reduced crop yields and stunted growth. Over the past decade, bioelectronic interfaces have evolved into miniaturized conformal electronic devices that integrate flexible monitoring systems with advanced electronic functionality. This development is largely attributable to advances in materials science, and micro/nanofabrication technology. The approach uses the mechanical and electronic properties of functional materials (polymer substrates and sensing elements) to create interfaces for plant monitoring. In addition to ensuring biocompatibility, several other factors need to be considered when developing these interfaces. These include the choice of materials, fabrication techniques, precision, electrical performance, and mechanical stability. In this review, some of the benefits plants can derive from several of the materials used to develop soft bioelectronic interfaces are discussed. The article describes how they can be used to create biocompatible monitoring devices that can enhance plant growth and health. Evaluation of these devices also takes into account features that ensure their long-term durability, sensitivity, and reliability. This article concludes with a discussion of the development of reliable soft bioelectronic systems for plants, which has the potential to advance the field of bioelectronics.

Predicting the quality attributes related to geographical growing regions in red-fleshed kiwifruit by data fusion of electronic nose and computer vision systems

The ability of a data fusion system composed of a computer vision system (CVS) and an electronic nose (e-nose) was evaluated to predict key physiochemical attributes and distinguish red-fleshed kiwifruit produced in three distinct regions in northern Iran. Color and morphological features from whole and middle-cut kiwifruits, along with the maximum responses of the 13 metal oxide semiconductor (MOS) sensors of an e-nose system, were used as inputs to the data fusion system. Principal component analysis (PCA) revealed that the first two principal components (PCs) extracted from the e-nose features could effectively differentiate kiwifruit samples from different regions. The PCA-SVM algorithm achieved a 93.33% classification rate for kiwifruits from three regions based on data from individual e-nose and CVS. Data fusion increased the classification rate of the SVM model to 100% and improved the performance of Support Vector Regression (SVR) for predicting physiochemical indices of kiwifruits compared to individual systems. The data fusion-based PCA-SVR models achieved validation R2 values ranging from 90.17% for the Brix-Acid Ratio (BAR) to 98.57% for pH prediction. These results demonstrate the high potential of fusing artificial visual and olfactory systems for quality monitoring and identifying the geographical growing regions of kiwifruits.

Tomato disease object detection method combining prior knowledge attention mechanism and multiscale features

To address the challenges of insufficient accuracy in detecting tomato disease object detection caused by dense target distributions, large-scale variations, and poor feature information of small objects in complex backgrounds, this study proposes the tomato disease object detection method that integrates prior knowledge attention mechanism and multi-scale features (PKAMMF). Firstly, the visual features of tomato disease images are fused with prior knowledge through the prior knowledge attention mechanism to obtain enhanced visual features corresponding to tomato diseases. Secondly, a new feature fusion layer is constructed in the Neck section to reduce feature loss. Furthermore, a specialized prediction layer specifically designed to improve the model's ability to detect small targets is incorporated. Finally, a new loss function known as A-SIOU (Adaptive Structured IoU) is employed to optimize the performance of the model in terms of bounding box regression. The experimental results on the self-built tomato disease dataset demonstrate the effectiveness of the proposed approach, and it achieves a mean average precision (mAP) of 91.96%, which is a 3.86% improvement compared to baseline methods. The results show significant improvements in the detection performance of multi-scale tomato disease objects.

Challenges and applications of volatile organic compounds monitoring technology in plant disease diagnosis

Biotic and abiotic stresses are well known to increase the emission of volatile organic compounds (VOCs) from plants. The analysis of VOCs emissions from plants enables timely diagnostic of plant diseases, which is critical for prompting sustainable agriculture. Previous studies have predominantly focused on the utilization of commercially available devices, such as electronic noses, for diagnosing plant diseases. However, recent advancements in nanomaterials research have significantly contributed to the development of novel VOCs sensors featuring exceptional sensitivity and selectivity. This comprehensive review presents a systematic analysis of VOCs monitoring technologies for plant diseases diagnosis, providing insights into their distinct advantages and limitations. Special emphasis is placed on custom-made VOCs sensors, with detailed discussions on their design, working principles, and detection performance. It is noteworthy that the application of VOCs monitoring technologies in the diagnostic process of plant diseases is still in its emerging stage, and several critical challenges demand attention and improvement. Specifically, the identification of specific stress factors using a single VOC sensor remains a formidable task, while environmental factors like humidity can potentially interfere with sensor readings, leading to inaccuracies. Future advancements should primarily focus on addressing these challenges to enhance the overall efficacy and reliability of VOCs monitoring technologies in the field of plant disease diagnosis.

Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics

Application of hyperspectral imaging (HSI) and data analysis algorithms was investigated for early and non-destructive detection of Botrytis cinerea infection. Hyperspectral images were collected from laboratory-based contaminated and non-contaminated fruits at different day intervals. The spectral wavelengths of 450 nm to 900 nm were pretreated by applying moving window smoothing (MWS), standard normal variates (SNV), multiplicative scatter correction (MSC), Savitzky-Golay 1^st derivative, and Savitzky-Golay 2^nd derivative algorithms. In addition, three different wavelength selection algorithms, namely; competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and successive projection algorithm (SPA), were executed on the spectra to invoke the most informative wavelengths. The linear discriminant analysis (LDA), developed with SNV-filtered spectral data, was the most accurate classifier to differentiate the contaminated and non-contaminated kiwifruits with accuracies of 96.67% and 96.00% in the cross-validation and evaluation stages, respectively. The system was able to detect infected samples before the appearance of disease symptoms. Results also showed that the gray-mold infection significantly influenced the kiwifruits' firmness, soluble solid content (SSC), and titratable acidity (TA) attributes. Moreover, the Savitzky-Golay 1^st derivative-CARS-PLSR model obtained the highest prediction rate for kiwifruit firmness, SSC, and TA with the determination coefficient (R²) values of 0.9879, 0.9644, 0.9797, respectively, in calibration stage. The corresponding cross-validation R² values were equal to 0.9722, 0.9317, 0.9500 for firmness, SSC, and TA, respectively. HSI and chemometric analysis demonstrated a high potential for rapid and non-destructive assessments of fungal-infected kiwifruits during storage.

A Low-Power Hardware Architecture for Real-Time CNN Computing

Convolutional neural network (CNN) is widely deployed on edge devices, performing tasks such as objective detection, image recognition and acoustic recognition. However, the limited resources and strict power constraints of edge devices pose a great challenge to applying the computationally intensive CNN models. In addition, for the edge applications with real-time requirements, such as real-time computing (RTC) systems, the computations need to be completed considering the required timing constraint, so it is more difficult to trade off between computational latency and power consumption. In this paper, we propose a low-power CNN accelerator for edge inference of RTC systems, where the computations are operated in a column-wise manner, to realize an immediate computation for the currently available input data. We observe that most computations of some CNN kernels in deep layers can be completed in multiple cycles, while not affecting the overall computational latency. Thus, we present a multi-cycle scheme to conduct the column-wise convolutional operations to reduce the hardware resource and power consumption. We present hardware architecture for the multi-cycle scheme as a domain-specific CNN architecture, which is then implemented in a 65 nm technology. We prove our proposed approach realizes up to 8.45%, 49.41% and 50.64% power reductions for LeNet, AlexNet and VGG16, respectively. The experimental results show that our approach tends to cause a larger power reduction for the CNN models with greater depth, larger kernels and more channels.

Potential of nanobiosensor in sustainable agriculture: the state-of-art

A rapid surge in world population leads to an increase in worldwide demand for agricultural products. Nanotechnology and its applications in agriculture have appeared as a boon to civilization with enormous potential in transforming conventional farming practices into redefined farming activities. Low-cost portable nanobiosensors are the most effective diagnostic tool for the rapid on-site assessment of plant and soil health including plant biotic and abiotic stress level, nutritional status, presence of hazardous chemicals in soil, etc. to maintain proper farming and crop productivity. Nanobiosensors detect physiological signals and convert them into standardized detectable signals. In order to achieve a reliable sensing analysis, nanoparticles can aid in signal amplification and sensor sensitivity by lowering the detection limit. The high selectivity and sensitivity of nanobiosensors enable early detection and management of targeted abnormalities. This study identifies the types of nanobiosensors according to the target application in agriculture sector.

Plant disease symptom segmentation in chlorophyll fluorescence imaging with a synthetic dataset

Despite the wide use of computer vision methods in plant health monitoring, little attention is paid to segmenting the diseased leaf area at its early stages. It can be explained by the lack of datasets of plant images with annotated disease lesions. We propose a novel methodology to generate fluorescent images of diseased plants with an automated lesion annotation. We demonstrate that a U-Net model aiming to segment disease lesions on fluorescent images of plant leaves can be efficiently trained purely by a synthetically generated dataset. The trained model showed 0.793% recall and 0.723% average precision against an empirical fluorescent test dataset. Creating and using such synthetic data can be a powerful technique to facilitate the application of deep learning methods in precision crop protection. Moreover, our method of generating synthetic fluorescent images is a way to improve the generalization ability of deep learning models.

Impedance Measures for Detecting Electrical Responses during Acute Injury and Exposure of Compounds to Roots of Plants

Electrical activity is widely used for assessing a plant's response to an injury or environmental stimulus. Commonly, a differential electrode recording between silver wire leads with the reference wire connected to the soil, or a part of the plant, is used. One method uses KCl-filled glass electrodes placed into the plant, similar to recording membrane/cell potentials in animal tissues. This method is more susceptible to artifacts of equipment noise and photoelectric effects than an impedance measure. An impedance measure using stainless steel wires is not as susceptible to electrically induced noises. Impedance measurements are able to detect injury in plants as well as exposure of the roots to environmental compounds (glutamate). The impedance measures were performed in 5 different plants (tomato, eggplant, pepper, liverwort, and Coleus scutellarioides), and responses to mechanical movement of the plant, as well as injury, were recorded. Monitoring electrical activity in a plant that arises in a distant plant was also demonstrated using the impedance method. The purpose of this report is to illustrate the ease in using impedance measures for monitoring electrical signals from individual plants or aggregates of plants for potentially scaling for high throughput and monitoring controlled culturing and outdoor field environments.