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For centuries, rice has been the one of the most important crops worldwide; over 3.5 billion of the population relies on rice as one of the most essential staple foods. However, rice diseases are responsible for enormous global economic losses due to the damage they cause to rice crops. Major rice diseases such as bacterial leaf blight (BLB), rice...
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Context 1
... infection on the collar region may destroy the whole leaf and can spread around the sheath, which ultimately becomes dry and dies (Chen et al. 2001;Barnwal et al. 2012;Larijani et al. 2019). Figure 3 illustrates the symptoms of rice blast disease caused by P. oryzae. ...
Context 2
... contrast with PCR, this technique has a shorter reaction time, while the sensitivity and specificity are almost the same or even better ( Chang et al. 2012;Lau et al. 2018). This technique is a highly exponential amplification technique that requires a set of four primers (FIP, BIP, F3, and B3), produces the target DNA at amounts of 10 9 -10 10 -fold with a dumbbell structure, and amplifies at a constant temperature (60-65°C) catalysed by Bst DNA polymerase within 30-60 min (Fig. 6) (Tomlinson and Boonham 2008). ...
Citations
... More than 50 kinds of rice fungal diseases, 6 kinds of bacterial diseases, 11 kinds of viral and mycoplasmosis, and 4 kinds of nematode diseases have been found in China [1][2][3]. The common diseases mainly include rice blast, sheath blight, white leaf blight, flax spot, rice stalk, rice grain smut and bad seedling [4][5][6]. ...
... (1) Lighter weight and smaller sizes need to be adopted to make the spore collection device smaller and more lightweight [24][25][26][27]. (2) The existing fixed spore capture equipment can obtain spores in glass slides, Eisenhoff bottles, scotch tape or bottle containers (as shown in Table 1), and the design of sample carriers also has room for research [28,29]. (3) We improve the work efficiency of mobile spore collection device by enhancing the ability of a single device that supports multiple sample collection tasks [21,30]. ...
Fungal spores that cause infectious fungal diseases in rice are mainly transmitted through air. The existing fixed, portable or vehicle-mounted fungal spore collection devices used for rice infectious diseases have several disadvantages, such as low efficiency, large volume, low precision and incomplete information. In this study, a mobile fungal spore collection device is designed, consisting of six filters called “Capture-A”, which can collect spores and other airborne particles onto a filter located on a rotating disc of six filters that can be rotated to a position allowing for the capture of six individual samples. They are captured one at a time and designed and validated by capturing spores above the rice field, and the parameters of the key components of the collector are optimized through fluid simulation and verification experiments. The parameter combination of the “Capturer-A” in the best working state is as follows: sampling vessel filter screen with aperture size of 0.150 mm, bent air duct with inner diameter of 20 mm, negative pressure fan with 1500 Pa and spore sampling of cylindrical shape. In the field test, the self-developed “Capturer-A” was compared with the existing “YFBZ3” (mobile spore collection device made by Yunfei Co., Ltd., Zhengzhou, China). The two devices were experimented on at 15 sampling points in three diseased rice fields, and the samples were examined and counted under a microscope in the laboratory. It was found that the spores of rice blast disease and rice flax spot disease of rice were contained in the samples; the number of samples collected by a single sampling vessel of “Capturer-A” was about twice that of the device “YFBZ3”in the test.
... Rice (Oryza sativa L) is a fundamental staple crop for humanity, and it has a profound influence on the survival and progress of human society (Cao et al., 2018). The cultivation of rice holds significant importance as a staple food crop for over three billion individuals globally (Azizi and Lau, 2022). Approximately 90% of the world's rice is produced in Asia and China is one of the largest global rice producers (Samal et al., 2022). ...
Introduction
Rice (Oryza sativa L.) is a pivotal cereal crop worldwide. It relies heavily on the presence of iron plaque on its root surfaces for optimal growth and enhanced stress resistance across diverse environmental conditions.
Method
To study the crystallographic aspects of iron plaque formation on rice roots, the concentrations of Fe²⁺ and PO4 ³⁻ were controlled in this study. The effects of these treatments were assessed through comprehensive analyzes encompassing root growth status, root surface iron concentration, root vitality, enzyme activities, and microstructural characteristics using advanced techniques such as root analysis, scanning electron microscopy (SEM), and ultrathin section transmission electron microscopy (TEM).
Results
The results demonstrated that an increase in the Fe²⁺ concentration or a decrease in the PO4 ³⁻ concentration in the nutrient solution led to improvements in various root growth indicators. There was an elevation in the DCB (dithionite-citrate–bicarbonate) iron content within the roots, enhanced root vitality, and a significant increase in the activities of the superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT) enzymes. Moreover, as the Fe²⁺ concentration increased, amorphous iron oxide minerals on the root surface were gradually transformed into ferrihydrite particles with sizes of approximately 200 nm and goethite particles with sizes of approximately 5 μm. This study showed that an increase in the Fe²⁺ concentration and a decrease in the PO4 ³⁻ concentration led to the formation of substantial iron plaque on the root surfaces. It is noteworthy that there was a distinct gap ranging from 0.5 to 3 μm between the iron plaque formed through PO4 ³⁻ treatment and the cellular layer of the root surface.
Discussion
This study elucidated the impacts of Fe²⁺ and PO4 ³⁻ treatments on the formation, structure, and morphology of the iron plaque while discerning variations in the spatial proximity between the iron plaque and root surface under different treatment conditions.
... Other comparative studies have also shown that consortium inoculation may be better than a single inoculation with PGPRs. Rice plants (Oryza sativa L.), one of the most important crops for the human population worldwide [96], were inoculated with a group of plant growth-promoting Bacillus strains ((Bacillus licheniformis (A21), B. haynesii (EN43), B. paralicheniformis (EN107), B. licheniformis (EN108), B. paralicheniformis (EN121), and B. haynesii (EN124)) and showed a higher biomass accumulation and increased grain yield [97]. Similarly, the synergistic action of two plant growth-stimulating bacteria, Pseudomonas putida and Bacillus amyloliquefaciens, was observed by the increasing tolerance to drought stress in Cicer arietinum L. Importantly, growth parameters such as root and shoot length and dry weights of roots and shoots were significantly higher in plants inoculated with the consortium than in the individual PGPR interaction [98]. ...
The growing human population has a greater demand for food; however, the care and preservation of nature as well as its resources must be considered when fulfilling this demand. An alternative employed in recent decades is the use and application of microbial inoculants, either individually or in consortium. The transplantation of rhizospheric microbiomes (rhizobiome) recently emerged as an additional proposal to protect crops from pathogens. In this review, rhizobiome transplantation was analyzed as an ecological alternative for increasing plant protection and crop production. The differences between single-strain/species inoculation and dual or consortium application were compared. Furthermore, the feasibility of the transplantation of other associated micro-communities, including phyllosphere and endosphere microbiomes, were evaluated. The current and future challenges surrounding rhizobiome transplantation were additionally discussed. In conclusion, rhizobiome transplantation emerges as an attractive alternative that goes beyond single/group inoculation of microbial agents; however, there is still a long way ahead before it can be applied in large-scale agriculture.
... Traditional methods of identifying these diseases require trained personnel to observe them, which is time-consuming and laborious. Often, by the time the disease is detected, it has progressed to a severe level, resulting in a loss of yield, time, and money [2]. Therefore, in this paper, we proposed a deep learning model with a deep residual architecture for identifying rice leaf diseases with a complex context in rice fields. ...
In this study, computer vision applicable to traditional agriculture was used to achieve accurate identification of rice leaf diseases with complex backgrounds. The researchers developed the RiceDRA-Net deep residual network model and used it to identify four different rice leaf diseases. The rice leaf disease test set with a complex background was named the CBG-Dataset, and a new single background rice leaf disease test set was constructed, the SBG-Dataset, based on the original dataset. The Res-Attention module used 3 × 3 convolutional kernels and denser connections compared with other attention mechanisms to reduce information loss. The experimental results showed that RiceDRA-Net achieved a recognition accuracy of 99.71% for the SBG-Dataset test set and possessed a recognition accuracy of 97.86% on the CBG-Dataset test set. In comparison with other classical models used in the experiments, the test accuracy of RiceDRA-Net on the CBG-Dataset decreased by only 1.85% compared with that on the SBG-Dataset. This fully illustrated that RiceDRA-Net is able to accurately recognize rice leaf diseases with complex backgrounds. RiceDRA-Net was very effective in some categories and was even capable of reaching 100% precision, indicating that the proposed model is accurate and efficient in identifying rice field diseases. The evaluation results also showed that RiceDRA-Net had a good recall ability, F1 score, and confusion matrix in both cases, demonstrating its strong robustness and stability.
... Rice is a cereal belonging to the genus Oryza and is one of the most important food crops in Asia. It is the staple food for about half of the world's population, 90% of which is produced in Asia [1,2]. Rice fungal disease can affect rice throughout the plant's growth cycle, resulting not only in a large area of reduced or no rice yield, but also directly threatening the quality of rice seed [3]. ...
As rice is one of the world’s most important food crops, protecting it from fungal diseases is very important for agricultural production. At present, it is difficult to diagnose rice fungal diseases at an early stage using relevant technologies, and there are a lack of rapid detection methods. This study proposes a microfluidic chip-based method combined with microscopic hyperspectral detection of rice fungal disease spores. First, a microfluidic chip with a dual inlet and three-stage structure was designed to separate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores in air. Then, the microscopic hyperspectral instrument was used to collect the hyperspectral data of the fungal disease spores in the enrichment area, and the competitive adaptive reweighting algorithm (CARS) was used to screen the characteristic bands of the spectral data collected from the spores of the two fungal diseases. Finally, the support vector machine (SVM) and convolutional neural network (CNN) were used to build the full-band classification model and the CARS filtered characteristic wavelength classification model, respectively. The results showed that the actual enrichment efficiency of the microfluidic chip designed in this study on Magnaporthe grisea spores and Ustilaginoidea virens spores was 82.67% and 80.70%, respectively. In the established model, the CARS-CNN classification model is the best for the classification of Magnaporthe grisea spores and Ustilaginoidea virens spores, and its F1-core index can reach 0.960 and 0.949, respectively. This study can effectively isolate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores, providing new methods and ideas for early detection of rice fungal disease spores.
... Xoo cells invade the rice plants through the wound opening on the leaves. At the initial stage, BLB symptoms appear as tannish-grey to white-yellowish lesions along the leaf's veins, and this alone causes yield losses up to 50% [3]. At advanced stages, however, BLB is difficult to distinguish from the bacterial leaf streak (BLS) disease, and although direct observation of the bacteria is preferred, the indication is not scientifically proven or confirmed. ...
... Hence the objective of this study is first to develop an immunobased strip for early detection of BLB disease in rice plants, and secondly, to demonstrate the strips' viability when attached to a custom-made portable biosensor device for on-site application. To the best of our knowledge, the latter has not been reported and ventured by any researchers in rice/plant disease management though several reports on plant disease biosensors have been published [3,7,12,13,14]. ...
We described here an electrochemical immunosensor strip based on a screen-printed carbon electrode (SPCE) for early detection of rice bacterial leaf blight (BLB) disease. The causal agent for this destructive disease has been identified as Xanthomonas oryzae pv. oryzae (Xoo). In order to circumvent the disease outbreak, an early detection system is required. Polyclonal antibody against Xoo was employed and immobilized on the SPCE strips modified with polypyrrole (PPy) and functionalized multi-walled carbon nanotube (fMWCNT) network. The anti-Xoo antibody is conjugated with horseradish peroxidase (HRP) as an enzyme label and used as the detection agent in the sensor development. Electrochemical detection was carried out via the chronoamperometry technique at a set potential of-200 mV. A fixed anti-Xoo antibody concentration at 0.03 mg/mL on the working electrode of the strip surface produced a standard linear curve for Xoo detection (R 2 = 0.9746). Two extraction methods for rice leaves (scissors-cutting and grinding) were compared for real samples application analysis. The Scissors-cutting method had less matrix interference effect and gave a higher recovery rate than the grinding method. The optimal immunosensor configuration was then compared with the PCR technique for Xoo detection in inoculated leaves in a controlled environment. A good correlation of 92.7% was achieved between the two methods. The immunosensor strips were then tested on an Android-based portable biosensor device for on-site detection of BLB in hotspot areas at Bagan Terap, Selangor Northwest and Sg. Burong, Tanjung Karang. On-field detection has indicated that the immunosensor strips can detect BLB disease as early as 15 days after transplant (DAT) before symptoms appear.
Sustainable agriculture represents the responsible utilization of natural resources while safeguarding the well-being of the natural environment. It encompasses the objectives of preserving the environment, fostering economic growth, and promoting socioeconomic equality. To achieve sustainable development for humanity, it is imperative to prioritize sustainable agriculture. One significant approach to achieving this transition is the extensive utilization of microbes, which play a crucial role due to the genetic reliance of plants on the beneficial functions provided by symbiotic microbes. This review focuses on the significance of rhizospheric microbial communities, also known as the rhizomicrobiome (RM). It is a complex community of microorganisms that live in the rhizosphere and influence the plant's growth and health. It provides its host plant with various benefits related to plant growth, including biocontrol, biofertilization, phytostimulation, rhizoremediation, stress resistance, and other advantageous properties. Yet, the mechanisms by which the RM contributes to sustainable agriculture remain largely unknown. Investigating this microbial population presents a significant opportunity to advance toward sustainable agriculture. Hence, this study aims to provide an overview of the diversity and applications of RM in sustainable agriculture practices. Lately, there has been growing momentum in various areas related to rhizobiome research and its application in agriculture. This includes rhizosphere engineering, synthetic microbiome application, agent-based modeling of the rhizobiome, and metagenomic studies. So, developing bioformulations of these beneficial microorganisms that support plant growth could serve as a promising solution for future strategies aimed at achieving a new green revolution.
Rice is an essential food crop that is cultivated in many countries. Rice leaf diseases can cause significant damage to crop cultivation, leading to reduced yields and economic losses. Traditional disease detection approaches are often time-consuming, labor-intensive, and require expertise. Automatic leaf disease detection approaches help farmers detect diseases without or with less human interference. Most of the earlier studies on rice leaf disease detection depended on image processing and machine learning techniques. Image processing techniques are used to extract features from diseased leaf images, such as the color, texture, vein patterns, and shape of lesions. Machine learning techniques are used to detect diseases based on the extracted features. In contrast, deep learning techniques learn complex patterns from large datasets without explicit feature extraction techniques and are well-suited for disease detection tasks. This systematic review explores various deep learning approaches used in the literature for rice leaf disease detection, such as Transfer Learning, Ensemble Learning, and Hybrid approaches. This review also discusses the effectiveness of these approaches in addressing various challenges. This review discusses the details of various models and hyperparameter settings used, model fine-tuning techniques followed, and performance evaluation metrics utilized in various studies. This review also discusses the limitations of existing studies and presents future directions for further developing more robust and efficient rice leaf disease detection techniques.
One of the essential components of human civilization is agriculture. It helps the economy in addition to supplying food. Plant leaves or crops are vulnerable to different diseases during agricultural cultivation. The diseases halt the growth of their respective species. Early and precise detection and classification of the diseases may reduce the chance of additional damage to the plants. The detection and classification of these diseases have become serious problems. Farmers’ typical way of predicting and classifying plant leaf diseases can be boring and erroneous. Problems may arise when attempting to predict the types of diseases manually. The inability to detect and classify plant diseases quickly may result in the destruction of crop plants, resulting in a significant decrease in products. Farmers that use computerized image processing methods in their fields can reduce losses and increase productivity. Numerous techniques have been adopted and applied in the detection and classification of plant diseases based on images of infected leaves or crops. Researchers have made significant progress in the detection and classification of diseases in the past by exploring various techniques. However, improvements are required as a result of reviews, new advancements, and discussions. The use of technology can significantly increase crop production all around the world. Previous research has determined the robustness of deep learning (DL) and machine learning (ML) techniques such as k-means clustering (KMC), naive Bayes (NB), feed-forward neural network (FFNN), support vector machine (SVM), k-nearest neighbor (KNN) classifier, fuzzy logic (FL), genetic algorithm (GA), artificial neural network (ANN), convolutional neural network (CNN), and so on. Here, from the DL and ML techniques that have been included in this particular study, CNNs are often the favored choice for image detection and classification due to their inherent capacity to autonomously acquire pertinent image features and grasp spatial hierarchies. Nevertheless, the selection between conventional ML and DL hinges upon the particular problem, the accessibility of data, and the computational capabilities accessible. Accordingly, in numerous advanced image detection and classification tasks, DL, mainly through CNNs, is preferred when ample data and computational resources are available and show good detection and classification effects on their datasets, but not on other datasets. Finally, in this paper, the author aims to keep future researchers up-to-date with the performances, evaluation metrics, and results of previously used techniques to detect and classify different forms of plant leaf or crop diseases using various image-processing techniques in the artificial intelligence (AI) field.
