Rice Plant Diseases Research Articles

Active compounds of Michelia champaca bark extract against Curvularia verruculosa fungi causing leaf spot disease in rice (Oryza sativa L.)

Curvularia verruculosa fungal causes leaf spot disease in rice plants. The bark extract of Michelia champaca could inhibit the growth of the fungi. The present research aimed to know the active compound responsible for antifungal activity. Extraction was done using the Maceration method, antifungal activity was measured using the Diffusion well method, and identification of active compounds was carried out using Gas chromatography-mass spectroscopy (GC-MS). The methanol extract obtained had a yield of 18.7%. It showed strong activity against C. verruculosa with an inhibition zone until 30.01 mm. The fractionation results showed that n-hexane extract (HE) was the strongest inhibiting the growth of C. verruculosa (32.45 mm), followed by chloroform extract (CE) (29.20 mm), while n-butanol extract (BE) was not active. Separating active compounds from HE extract was made using Column chromatography (CC) method with silica gel as the stationary phase and the mixture of n-hexane-acetone (3:1) as the mobile phase. This separation resulted in 5 combined fractions; HE3 and HE5 extracts showed very strong activity against C. verruculosa, with a diameter of the inhibition zone of 26.73 and 33.46 mm, respectively; HE2 extract showed strong activity with a diameter of the inhibition zone of 15.21 mm, while HE1 and HE4 extracts did not show activity. Identification using GC-MS, especially the HE3 extract, revealed that the extract contained two compounds: tributyl acetyl citrate and terephthalic acid, dodecyl-2-ethylhexyl ester. The result indicated that the bark extract of M. champaca had the potential to be a botanical fungicide.

One pot synthetic approach to 2‐methyl‐5‐phenyl‐7‐amino‐[1,3,4]‐thiadiazolo[3,2‐a]pyrimidine‐6‐carbonitrile derivatives with antifungal evaluation

AbstractNovel 2‐methyl‐5‐phenyl‐7‐amino‐[1,3,4]‐thiadiazolo[3,2‐a]pyrimidine‐6‐carbonitrile derivatives 3 were synthesized from 2‐amino‐5‐methyl‐1,3,4‐thiadiazole 1 and benzylidinemalononitrile 2 in ethanol by refluxing the mixture with catalytic amount of NaOH. The structures of all compounds were characterized by elemental analysis, IR, MS, and 1H nuclear magnetic resonance spectra. The newly synthesized compounds were also seen as an affective fungicide against Rhizoctonia solani and Trichoderma sp., two fungal microbes causing diseases in rice plants.

MobileNetV2-Incep-M: a hybrid lightweight model for the classification of rice plant diseases

MobileNetV2-Incep-M: a hybrid lightweight model for the classification of rice plant diseases

Evaluation of phyllosphere bacterial biocontrol agents for the suppression of rice foliar diseases

AbstractPhyllosphere colonizing antagonistic bacterial isolates were evaluated for managing major rice foliar diseases, namely brown spot caused by Bipolaris oryzae and sheath rot caused by Sarocladium oryzae. Among these various isolates, Pseudomonas aeruginosa isolate 1 effectively inhibited the mycelial growth of B. oryzae and S. oryzae. The volatile compounds of Bacillus subtilis isolates effectively inhibited the mycelial growth of S. oryzae and B. oryzae. Non‐volatile compounds of P. aeruginosa isolate 2 and B. subtilis isolate 1 recorded the maximum inhibition of mycelial growth of B. oryzae and S. oryzae, respectively. P. aeruginosa isolates produced fluorescent pigments and hydrogen cyanide (HCN), whereas isolates of Bacillus spp. did not. All the isolates of P. aeruginosa and Bacillus spp. produced protease enzymes. P. aeruginosa isolates solubilized phosphate, whereas isolates of Bacillus spp. did not. Rice seeds treated with these bacterial suspensions showed improvement in plant growth parameters. The foliar application of two bacterial strains, namely P. aeruginosa isolate 1 + B. subtilis isolate 1 recorded the minimum brown spot and sheath rot disease incidence. Thus, the present showed that phyllosphere colonizing P. aeruginosa and B. subtilis have the potential application for managing foliar fungal diseases of rice plants.

Utilization of Artificial Neural Network in Rice Plant Disease Classification Using Leaf Image

Rice is the name of the type of plant that is needed by humans in the world. The plant is used as the main source of energy by Most people in the world, especially on the Asian continent. The importance of rice plants makes rice widely planted in various regions. Most humans use rice as a staple crop. Therefore, rice production needs to be considered to meet the need for enough food for most people in the world. The main thing that needs to be considered in maximizing rice production is that when guarding rice plants, many factors that inhibit rice plants can be the cause of food crises in various regions. Therefore, the care of rice production needs to be considered. In addition to the lack of nutrients in water and soil in decreasing rice production, plant diseases also need to be considered. Some types of diseases that often attack rice plants include bacterial leaf blight, brown spots, and left smut. Therefore, there is knowledge of prevention efforts or early treatment before the disease attacks rice plants more widely. The efficacy of technology can be used in solving this problem, we can take advantage of artificial intelligence in it. Artificial intelligence is implemented for the detection of types of diseases in rice plants using image images on the leaves of rice plants. If the disease in rice plants can be detected, it will make it easier for rice plant farmers to overcome the disease. The ANN (Artificial neural network) algorithm can be used in this problem from the results of research on identifying the type of rice disease using the algorithm obtained an accuracy of 83%. This shows the ability of artificial intelligence in disease identification can help farmers detect types of diseases.

Augmented Rice Plant Disease Detection with Convolutional Neural Networks

The recognition and classification of rice plant diseases require an accurate system to generate classification data. Types of rice diseases can be identified in several ways, one of which is leaf characterization. One method that has high accuracy in identifying plant disease types is Convolutional Neural Networks (CNN). However, the rice disease data used has unbalanced data which affects the performance of the method. Therefore, the purpose of this research was to apply data augmentation to handle unbalanced rice disease data to improve the performance of the Convolutional Neural Network (CNN) method for rice disease type detection based on leaf images. The method used in this research is the CNN method for detecting rice disease types based on leaf images. The result of this research was the CNN method with 100 epochs able to produce an accuracy of 99.7% in detecting rice diseases based on leaf images with a division of 80% training data (2438 data) and 20% testing data (608 data). The conclusion is that the CNN method with the augmentation process can be used in rice disease detection because it has very high accuracy.

The Empirical Study On Algorithm Optimization In Expert Systems For Diagnosing Rice Plant Diseases

Rice is one of the most important cultivated plants for human survival. The activity of cultivating rice plants becomes a livelihood for most of these residents, so the success rate of the amount of rice harvested becomes very important because they depend on how much rice can be harvested, disease diagnosis is very important for farmers, this is very important to reduce economic losses due to diseases that cause crop failure. Therefore, when dealing with rice diseases, an expert is needed to make diagnoses or solutions to rice diseases. However, an expert does not know when to come to the village, and farmers also do not understand all rice diseases. Therefore, a web-based expert system application using the forward chaining method is proposed to represent an expert to help farmers diagnose and solve diseases of rice plants with existing symptoms.

Application of Convolutional Neural Network ResNet-50 V2 on Image Classification of Rice Plant Disease

Rice is the most important crop in global food security and socioeconomic stability. A part of the world's population makes rice a food requirement but the problem is found that all rice varieties suffer from several diseases and pests. Therefore, it is necessary to ensure the quality of healthy and proper rice growth by detecting diseases present in rice plants and treatment of affected plants. In this study, the Convolutional Neural Network (CNN) algorithm was applied in classifying diseases on the leaves of rice plants by experimenting with several parameters and architecture to get the best accuracy. This study was conducted image classification of rice plant disease using CNN architecture ResNet-50V2 with data using preprocessing Augmentation. The test was conducted with three optimizers such as SGD, Adam, and RMSprop by combining various parameters, namely epoch, batch size, learning rate, and SGD and RMSprop optimizers. Division of image data with 70:30 ratio of training data and test data; 80:20; 90:10. From these results, it was found that Adam was the best optimizer in the 80:20 data division in this study with an accuracy level of 0.9992, followed by the SGD optimizer with an accuracy level of 0.9983, while the RMSProp optimizer was ranked third with an accuracy level of 0.9978.

Detection and prediction of rice plant diseases using convolutional neural network (CNN) method

Rice is a basic staple food in many Asian countries and is generally irreplaceable. Rice accounts for almost half of Asia food expenditure. Rice is too a crop that is prone to plant disease. It can appear and cause a decline in the quality of rice. However, constant monitoring of the rice fields can prevent the infection of the disease. Therefore, detection and prediction of rice plant diseases is one of the topics that will be discussed in this research. The purpose of this research is to help farmers to quickly pinpoint the disease of rice plants and take care of it properly. The methods used in this paper is researching and redesigning the previous attempt to hopefully make it better and more accurate. We will be using Convolutional Neural Network (CNN) models VGG16 as our algorithm. The results are that our proposed method has more accuracy than previous research using a similar dataset. The novelty of this paper is the increased accuracy of rice plant disease detection.

An Improved Deep Learning Model for Detecting Rice Diseases

Early detection of rice plant diseases could help to quickly eradicate numerous diseases, such as fungi, viruses, and bacteria, consequently increasing rice yield. Traditional techniques for performing this task may not be the best because they take a long time, require experienced personnel, and are susceptible to a variety of infections. As a result, machine learning and deep learning approaches have recently been utilized to overcome these issues and present a more accurate model for detecting rice plant diseases. However, the current machine learning (ML) and deep learning (DL) models for this task produce unsatisfactory results due to many constraints, including high computational expenses and overfitting. To address these limitations and obtain more accurate disease detection for rice plants, we present a hybrid model of MobileNet and DNN (HMobileNetDNN). The small size of MobileNet minimizes computing costs, while the depth and complexity of DNN improve the model's capacity to capture complicated features, yielding satisfying results. Furthermore, the proposed HMobileNetDNN is also compared to four transfer learning-based DL models, namely ResNetV2, InceptionV3, MobilenetV2, and DensNet121, using the Paddy Doctor dataset. We employ several performance metrics to assess the effectiveness and efficiency of the models, like accuracy, precision, recall, F1 score, and area under the curve. The proposed model outperformed the comparing models, achieving values of 0.918, 0.918, 0.907, 0.912, and 0.949 for accuracy, precision, recall, F1 score, and area under the curve, respectively.

Effect of Application of Endophytic Bacteria on the Development of Bacterial Leaf Blight Disease, Growth, and Production of Rice Plants

Bacterial leaf blight can be found in various stages of rice plant growth, so this disease can affect the growth and production of rice plants. This research aims to determine the effect of applying an endophytic bacterial formula to rice seedlings on the development of HDB disease, growth, and production of rice plants. This study used 4 isolates of endophytic bacteria (isolates BE-KH1, BE-M2, BE-M3, and BE-S5) which were formulated in coconut water waste + peptone. The results of the study showed that the application of the endophytic bacterial formula to rice seedlings was able to reduce the severity of bacterial leaf blight by 54,5 – 63,2% and had an effect on growth, full grain weight, and rice grain weight in rice plants inoculated with Xanthomonas oryzae pv. oryzae bacteria.

In vitro production of gibberellic acid and fusaric acid by Fusarium spp. and their role in bakanae disease development

Gibberellic acid (GA3) and fusaric acid (FA) are directly correlated with the symptom development of bakanae disease in rice plants. The role of GA3 and FA, produced in vitro by several Fusarium species associated with bakanae disease, was studied to understand the disease’s etiology and its impact on symptom development. In total, 121 Fusarium strains were obtained from bakanae-infected rice plants collected from various rice cultivation regions in Bangladesh. Finally, 18 highly virulent Fusarium strains were selected based on virulence assay and further evaluated for GA3 and FA production through ultra-performance liquid chromatography analysis. Among the Fusarium strains, Fusarium fujikuroi strains produced a high amount of GA3 and a low amount of FA. In contrast, Fusarium proliferatum and Fusarium verticillioides strains produced a high amount of FA and a low amount of GA3. In exception, the Fusarium strain BD117R of F. fujikuroi produced no GA3 but a high amount of FA. Interestingly, Fusarium commune produced only FA in high concentration. In bakanae disease development, GA3 production was positively related to elongation symptoms whereas FA contributes to stunting symptoms. This is the first record of the production of GA3 and FA by Fusarium species causing bakanae disease of rice in Bangladesh.

A novel rice plant leaf diseases detection using deep spectral generative adversarial neural network

The farming industry widely requires automatic detection and analysis of rice diseases to avoid wasting financial and other resources, reduce yield loss, improve processing efficiency, and obtain healthy crop yields. The proposed Deep Spectral Generative Adversarial Neural Network (DSGAN2) method is used for detecting rice plant leaf disease. Initially, fed into the input of healthy and non-healthy leaves from the collected dataset. Then, apply an Improved Threshold Neural Network (ITNN) method to enhance the image quality. Next, it uses a Segmentation using a Segment Multiscale Neural Slicing (SMNS) algorithm to identify the support-intensive color saturation based on the enhanced image. After that, the Spectral Scaled Absolute Feature Selection (S2AFS) method is applied to select optimal features and the closest weight from segmented rice plant leaves. Social Spider Optimization will select the feature using the Closest Weight (S2O-FCW) algorithm to analyze the feature weight values. Finally, the proposed Soft-Max Logistic Activation Function with Deep Spectral Generative Adversarial Neural Network (DSGAN2) algorithm detects rice plant disease based on selected features. With an accuracy of 97 %, the model helps farmers identify and identify Rice Plant diseases. The proposed system Deep Spectral Generative Adversarial Neural Network (DSGAN2) produces a decreasing false rate compared to the existing system of ACPSOSVM-Dual Channels Convolutional Neural Network (APS-DCCNN) is 55.2 %, Alex Net is 50.4 %, and Convolutional Neural Network (CNN) is 49.5 %.

A novel approach using machine learning to detect and classify rice plant diseases

A novel approach using machine learning to detect and classify rice plant diseases

Determining the potential of sonic bloom in inducing rice resistance (Oryza sativa) against the attack of bacterial leaf blight disease caused by Xanthomonas oryzae

Xanthomonas oryzae which causes bacterial leaf blight disease is one of the main pathogens that attack rice crops. The threat of losing rice yields due to X. oryzae needs to be avoided by implementing appropriate control strategies. Sonic bloom has the potential to induce plant immunity against diseases. Hence, this study aimed to examine the potential of sonic bloom in inducing rice resistance to bacterial leaf blight. The experiment was arranged in a Randomized Block Design (RBD) with five treatments and three replications. The treatments consisted of several frequencies, namely PR (0.5 - 1 kHz and inoculated with X. oryzae), PS (3 - 5 kHz and inoculated with X. oryzae), PT (7 - 10 kHz and inoculated with X. oryzae), KP (inoculated with X. oryzae without sonic bloom treatment), KN (no bacterial inoculation and sonic bloom treatment). The results showed that all sonic bloom treatments (LF, MF, HF) had a significant effect on increasing the intensity of BLB disease in rice plants. This showed that sonic bloom in this range does not have the potential to induce rice plant resistance to BLB. Further research to find out the causes of this is needed.

RICE DISEASE RECOGNITION USING TRANSFER LEARNING XCEPTION CONVOLUTIONAL NEURAL NETWORK

As one of the major rice producers, Indonesia faces significant challenges related to plant diseases such as blast, brown spot, tugro, leaf smut, and blight. These diseases threaten food security and result in economic losses, underscoring the importance of early detection and management of rice diseases. Convolutional Neural Network (CNN) has proven effective in detecting diseases in rice plants. Specifically, transfer learning with CNN, particularly the Xception model, has the advantage of efficiently extracting automatic features and performing well even with limited datasets. This study aims to develop the Xception model for rice disease recognition based on leaf images. Through the fine-tuning process, the Xception model achieved accuracies, precisions, recalls, and F1-scores of 0.89, 0.90, 0.89, and 0.89, respectively, on a dataset with a total of 320 images. Additionally, the Xception model outperformed VGG16, MobileNetV2, and EfficientNetV2.

Expert System for Pest Diagnosis on Local Black Rice Plant in East Kalimantan Using the Naive Bayes Method

Rice plant is a food crop that produces rice as the staple food for the majority of Indonesian people. Local rice which significantly contributes to fulfill the national rice consumption is black rice produced in East Kalimantan. However, local black rice often experiences crop failure due to pest attacks and environmental factors. The amount of local black rice production also continues to decrease due to limited human resources who have the skills and knowledge to diagnose pests in black rice plants. Therefore, one effort that can be made to overcome this problem is to create an expert system that can diagnose pests and diseases in black rice plants. The expert system in this research uses the Naive Bayes method, which identifies 11 types of pests that attack black rice plants and 34 symptoms caused by these pest attacks. Naive Bayes can provide information about the percentage of pests that rice plants might experience. Based on the results of the test cases, an accuracy value of80% was obtained, so the expert system built in this research can diagnose pests on black rice plants quite well.

Transformasi Pertanian: Memanfaatkan Limbah Kulit Bawang Merah Sebagai Pestisida Ramah Lingkungan dan Pupuk Organik yang Aman Bagi Petani

Problems that cannot be separated from the agricultural sector, especially in Kec. Percut Sei Tuan, Kab. Deli Serdang is the use of chemical pesticides in agriculture. The use of pesticides in agriculture can have positive and negative consequences for the environment and also the health of farmers. The abundance of onion skin waste is another way that can be optimized for its use as a standard ingredient for making organic pesticides. The aim of this research is to transform the use of chemical pesticides into organic pesticides. The practical method used in this journal is qualitative. Making pesticides from onion skin waste can be used to control pest attacks and rice plant diseases. Using onion skin waste as an environmentally friendly pesticide is a more sustainable alternative and has a smaller negative impact on the environment compared to using chemical pesticides.

Using Deep Learning and Machine Learning: Real-Time Discernment and Diagnostics of Rice-Leaf Diseases in Bangladesh

Bangladesh is heavily reliant on rice production, but a staggering annual decline of 37% in rice output due to insufficient knowledge in recognizing and managing rice plant diseases has raised concerns. As a result, there is a pressing need for a system that can accurately identify and control rice plant diseases automatically. CNNs have demonstrated their effectiveness in detecting plant diseases, thanks to their exceptional image classification capabilities. Nevertheless, research on rice plant disease identification remains scarce. This study offers a comprehensive overview of rice plant ailments and explores DL techniques used for their detection. By evaluating the advantages and disadvantages of various systems found in the literature, the study aims to identify the most accurate means of detecting and controlling rice plant diseases using DL techniques. We present a real-time detection and diagnostic system for rice lead diseases that utilizes ML methods. This system is designed to identify three prevalent rice plant diseases, specially leaf smut, bacterial leaf blight and brown spot diseases. Clear images of affected rice leaves against a white background serve as input data for the system. To train the dataset, several ML algorithms were employed including KNN, Naive Bayes, J48 and Logistic Regression. Following the pre-processing stage, the decision tree algorithm demonstrated an accurateness of over 97% when claimed to test dataset. In conclusion, implementing an automated system that leverages ML techniques is vital for reducing the time and labor required for detecting and managing rice plant diseases. Such a system would contribute significantly to ensuring the healthy growth of rice plants in Bangladesh, ultimately boosting the nation’s rice production.

Advances in Rice Plant Disease Detection: A Survey of Machine Learning and Deep Learning Approaches

Advances in Rice Plant Disease Detection: A Survey of Machine Learning and Deep Learning Approaches