Classification of Rice Leaf Blast Severity Using Hyperspectral Imaging

Background: Hyperspectral imaging is an emerging technology applied in plant disease research, including disease detection, multiple disease identification, disease severity assessment, and disease resistance evaluation. Rice leaf blast is prevalent all over the world and is a serious threat to rice yield and quality. In this paper, the standard deviation (STD) of the spectral reflectance of whole leaves was calculated and a support vector machine (SVM) model was built to classify the degree of rice leaf blast at different growth stages.Results: The classification accuracy of the full-spectrum-based SVM model at jointing stage, booting stage and heading stage was 94.44%, 81.58% and 80.48%, respectively. The corresponding macro recall values were 0.9714, 0.715 and 0.79. The average STD of the spectral reflectance of the whole leaf differed not only within samples with different disease grades, but also those with the same disease level. Conclusion: The STD of the spectral reflectance of whole leaf could be utilized to classify the rice leaf blast degree at different growth stages. The classification method was derived from physiological phenomena that were visible to the naked eye, making it more intuitive and convincing.

Combination of Strobilurin and Triazole Chemicals for the Management of Blast Disease in Mushk Budji -Aromatic Rice

Rice blast is considered one of the most important fungal diseases of rice. Although diseases can be managed by using resistant cultivars, the blast pathogen has successfully overcome the single gene resistance in a short period and rendered several varieties susceptible to blast which were otherwise intended to be resistant. As such, chemical control is still the most efficient method of disease control for reducing the losses caused due to diseases. Field experiments were conducted over two successive years, 2018 and 2019, in temperate rice growing areas in northern India. All the fungicides effectively reduced leaf blast incidence and intensity, and neck blast incidence under field conditions. Tricyclazole proved most effective against rice blast and recorded a leaf blast incidence of only 8.41%. Among the combinations of fungicides, azoxystrobin + difenoconazole and azoxystrobin + tebuconazole were highly effective, recording a leaf blast incidence of 9.19 and 10.40%, respectively. The chemical combination mancozeb + carbendazim proved less effective in controlling the blast and it recorded a disease incidence of 27.61%. A similar trend was followed in neck blast incidence with tricyclazole, azoxystrobin + difenoconazole, and azoxystrobin + tebuconazole showing the highest levels of blast reductions. It is evident from the current study that the tested fungicide combinations can be used as alternatives to tricyclazole which is facing the challenges of fungicide resistance development and other environmental concerns and has been banned from use in India and other countries. The manuscript may provide a guideline of fungicide application to farmers cultivating susceptible varieties of rice.

Optimization of the Extraction Process and Application of Bacterial Extracts in the Control of Brown Spot and Leaf Blast in Rice Culture

The objective of this work was to optimize the extraction process and application of bacterial extracts of Bacillus sp. and Serratia sp. in leaf blast control (Magnapothe oryzae) and brown spot (Bipolaris oryzae) in rice culture. The work was divided into three stages: 1) Bacterial obtaining extracts through liquid-liquid extraction 2) Antagonistic capacity of bacterial extracts to M. oryaze and B. oryae 3) Suppression of brown spot (A1) and leaf blast (A2) in greenhouse. The bacterial isolates in present study were identified as Bacillus sp. (BRM32110) and Serratia marcescens (BRM32113). The crude extract of both isolates at different extraction times 6, 16, 24, 48 and 72 hours reduced the growth of colonies of M. oryzae and B. oryzae by up to 92% and 28%, respectively. The extracts that showed highest inhibition of colony growth were those obtained after 6 and 16h of incubation and were selected for subsequent assays. These, for both isolates were able to reduce conidia germination by up to 91% and apressorium formation of M. oryzae by up to 93%. In green house, A1 the treatment that stood out was the extract of Bacillus sp. (16h) with 6.7% of leaf area affected and in A2 the treatment S. marcescens extract (16h) stood out with only 7.6% of leaf area affected with brusone when compared to control. The use of extracts of Bacillus sp. and Serratia marcescens was efficient in reducing the severity of brown spot and leaf blast in rice crop.

Evaluation of combination spray schedules of Tricyclazole and Pseudomonas fluorescens against blast disease of rice

Rice is the most important food crop and directly feeding more people than any other crop in the world.The blast disease caused by Pyricularia oryzae is a major and foremost constraint in rice production.Use of tricyclazole fungicide is one of the best and cost effective ways to quickly manage the disease. Considering the extensive use of fungicide has adverse effects for environment, the present study has been conducted to know the effect of Tricyclazole and Pseudomonas fluorescens alone and various combinations during rabi 2015-2016 and 2016-2017. From the two years data, the results indicated that a spray schedule consisting of tricyclazole followed by an alternate spray of Pseudomonas fluorescens (T + P + T + P), as well as either seed treatment with tricyclazole at 0.1 percent or root dipping with P. fluorescens at 1%, effectively reduced both leaf blast and neck blast with increasing grain yield.

Automatic Diagnosis of Rice Diseases Using Deep Learning

Rice disease has serious negative effects on crop yield, and the correct diagnosis of rice diseases is the key to avoid these effects. However, the existing disease diagnosis methods for rice are neither accurate nor efficient, and special equipment is often required. In this study, an automatic diagnosis method was developed and implemented in a smartphone app. The method was developed using deep learning based on a large dataset that contained 33,026 images of six types of rice diseases: leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot. The core of the method was the Ensemble Model in which submodels were integrated. Finally, the Ensemble Model was validated using a separate set of images. Results showed that the three best submodels were DenseNet-121, SE-ResNet-50, and ResNeSt-50, in terms of several attributes, such as, learning rate, precision, recall, and disease recognition accuracy. Therefore, these three submodels were selected and integrated in the Ensemble Model. The Ensemble Model minimized confusion among the different types of disease, reducing misdiagnosis of the disease. Using the Ensemble Model to diagnose six types of rice diseases, an overall accuracy of 91% was achieved, which is considered to be reasonably good, considering the appearance similarities in some types of rice disease. The smartphone app allowed the client to use the Ensemble Model on the web server through a network, which was convenient and efficient for the field diagnosis of rice leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot.

Rice Leaf Blast Classification Method Based on Fused Features and One-Dimensional Deep Convolutional Neural Network

Rice leaf blast, which is seriously affecting the yield and quality of rice around the world, is a fungal disease that easily develops under high temperature and humidity conditions. Therefore, the use of accurate and non-destructive diagnostic methods is important for rice production management. Hyperspectral imaging technology is a type of crop disease identification method with great potential. However, a large amount of redundant information mixed in hyperspectral data makes it more difficult to establish an efficient disease classification model. At the same time, the difficulty and small scale of agricultural hyperspectral imaging data acquisition has resulted in unrepresentative features being acquired. Therefore, the focus of this study was to determine the best classification features and classification models for the five disease classes of leaf blast in order to improve the accuracy of grading the disease. First, the hyperspectral imaging data were pre-processed in order to extract rice leaf samples of five disease classes, and the number of samples was increased by data augmentation methods. Secondly, spectral feature wavelengths, vegetation indices and texture features were obtained based on the amplified sample data. Thirdly, seven one-dimensional deep convolutional neural networks (DCNN) models were constructed based on spectral feature wavelengths, vegetation indices, texture features and their fusion features. Finally, the model in this paper was compared and analyzed with the Inception V3, ZF-Net, TextCNN and bidirectional gated recurrent unit (BiGRU); support vector machine (SVM); and extreme learning machine (ELM) models in order to determine the best classification features and classification models for different disease classes of leaf blast. The results showed that the classification model constructed using fused features was significantly better than the model constructed with a single feature in terms of accuracy in grading the degree of leaf blast disease. The best performance was achieved with the combination of the successive projections algorithm (SPA) selected feature wavelengths and texture features (TFs). The modeling results also show that the DCNN model provides better classification capability for disease classification than the Inception V3, ZF-Net, TextCNN, BiGRU, SVM and ELM classification models. The SPA + TFs-DCNN achieved the best classification accuracy with an overall accuracy (OA) and Kappa of 98.58% and 98.22%, respectively. In terms of the classification of the specific different disease classes, the F1-scores for diseases of classes 0, 1 and 2 were all 100%, while the F1-scores for diseases of classes 4 and 5 were 96.48% and 96.68%, respectively. This study provides a new method for the identification and classification of rice leaf blast and a research basis for assessing the extent of the disease in the field.

Spatiotemporal Survey of Multiple Rice Diseases in Irrigated Areas Compared to Rainfed Lowlands in the Western Burkina Faso

Multiple constraints affect rice yields and global production in West Africa. Among these constraints are viral, bacterial and fungal pathogens. We aimed to describe the spatiotemporal patterns of occurrence and incidence of multiple rice diseases in farmers’ fields in contrasting rice growing systems in western Burkina Faso. For this purpose, we selected a set of three pairs of sites, each comprising an irrigated area and a neighboring rainfed lowland, and studied them over four consecutive years. We first performed interviews with the rice farmers to better characterize the management practices at the different sites. This study revealed that the transplanting of rice and the possibility of growing rice twice a year are restricted to irrigated areas, while other practices, such as the use of registered rice cultivars, fertilization and pesticides, are not specific but differ between the two rice growing systems. Then, we performed symptom observations at these study sites to monitor the following four diseases: yellow mottle disease, Bacterial Leaf Streak (BLS), rice leaf blast and brown spot. The infection rates were found to be higher in irrigated areas than in rainfed lowlands, both when analyzing all observed symptoms together (any of the four diseases) and when specifically considering each of the two diseases: BLS and rice leaf blast. Brown spot was particularly prevalent in all six study sites, while yellow mottle disease was particularly structured geographically. Various diseases were frequently found together in the same field (co-occurrence) or even on the same plant (coinfection), especially in irrigated areas.

Macroscopic Diagnosis of Plant Diseases Caused by Pathogenic Microorganism

From a biological point of view, plant diseases are deviations from internal traits that cause plants to not be able to carry out normal growth activities. Plant diseases in the field can be identified based on signs and symptoms of diseases that appear. This study aims to determine how to diagnose macroscopically the symptoms of diseases that arise due to pathogenic microorganisms on plants. This research was conducted in 3 stages which included the location survey, observation, and primary data collection stages. The method for primary data collection is done by direct observation, namely direct observation of samples and documented. Data were analyzed using the formula to calculate the percentage and intensity of disease attacks on plants. The results obtained were as many as 3 plant species (Capsicum sp, Solanum escelentum, Oryza sativa) from 12 plants which were observed to be attacked by pathogenic microorganisms with a percentage amount (22,45 %, 58,97%, 9%). Obtain 4 types of pathogenic microorganisms that attack plants, namely Gemini virus that causes leaf curling in Capsicum sp plant, Pyricularia oryzae fungus which causes leaf blast in Oryza sativa plants, the fungus Alternaria solani causes dry spot and Rhizoctonia solani fungus which causes leaf blast in Oryza sativa plants, the fungus Alternaria solani causes leaf dry spot and Rhizoctonia solani fungi that cause fruit rot in Solanum escelentum.