Rice Brown Spot Research Articles

Proximally sensed RGB images and colour indices for distinguishing rice blast and brown spot diseases by k-means clustering: Towards a mobile application solution

Rice blast (RB) and Brown spot (BS) are economically important diseases in rice that cause greater yield losses annually. Both share the same host and produce quite similar lesions, which leads to confusion in identification by farmers. Proper identification is essential for better management of the diseases. Visual identification needs trained experts and the laboratory-based experiments using molecular techniques are costly and time-consuming even though they are accurate. This study investigated the differentiation of the lesions from these two diseases based on proximally sensed digital RGB images and derived colour indices. Digital images of lesions were acquired using a smartphone camera. Thirty-six colour indices were evaluated by k-means clustering to distinguish the diseases using three colour channel components; RGB, HSV, and La*b*. Briefly, the background of the images was masked to target the leaf spot lesion, and colour indices were derived as features from the centre region across the lesion, coinciding with the common identification practice of plant pathologists. The results revealed that 36 indices delineated both diseases with 84.3 % accuracy. However, it was also found that the accuracy was mostly governed by indices associated with the R, G and B profiles, excluding the others. G/R, NGRDI, (R + G + B)/R, VARI, (G + B)/R, R/G, Nor_r, G-R, Mean_A, and Logsig indices were identified to contribute more in distinguishing the diseases. Therefore, these RGB-based colour indices can be used to distinguish blast and brown spot diseases using the k-means algorithm. The results from this study present an alternative, and non-destructive, objective method for identifying RB and BS disease symptoms. Based on the findings, a mobile application, Blast O spot is developed to differentiate the diseases in fields.

Optimized Crop Disease Identification in Bangladesh: A Deep Learning and SVM Hybrid Model for Rice, Potato, and Corn.

Agriculture plays a vital role in Bangladesh's economy. It is essential to ensure the proper growth and health of crops for the development of the agricultural sector. In the context of Bangladesh, crop diseases pose a significant threat to agricultural output and, consequently, food security. This necessitates the timely and precise identification of such diseases to ensure the sustainability of food production. This study focuses on building a hybrid deep learning model for the identification of three specific diseases affecting three major crops: late blight in potatoes, brown spot in rice, and common rust in corn. The proposed model leverages EfficientNetB0's feature extraction capabilities, known for achieving rapid high learning rates, coupled with the classification proficiency of SVMs, a well-established machine learning algorithm. This unified approach streamlines data processing and feature extraction, potentially improving model generalizability across diverse crops and diseases. It also aims to address the challenges of computational efficiency and accuracy that are often encountered in precision agriculture applications. The proposed hybrid model achieved 97.29% accuracy. A comparative analysis with other models, CNN, VGG16, ResNet50, Xception, Mobilenet V2, Autoencoders, Inception v3, and EfficientNetB0 each achieving an accuracy of 86.57%, 83.29%, 68.79%, 94.07%, 90.71%, 87.90%, 94.14%, and 96.14% respectively, demonstrated the superior performance of our proposed model.

Identification of plant based potential antifungal compounds against BMK-1 protein of Bipolaris oryzae using molecular docking approach

Rice brown spot is an important disease of rice worldwide that inflicts substantial yield losses. The antimicrobial potential of methanol, acetone and dimethyl sulfoxide (DMSO) extracts of different medicinal plants, viz., Syzygium aromaticum, Saussurea costus, Acorus calamus, Bergenia ciliate, Geranium pratense, Mentha longifolia, Inula racemosa, Podophyllum hexandrum, Heracleum candicans and Picrorhiza kurroa, against the brown spot pathogen Bipolaris oryzae in vitro was evaluated via mycelial growth inhibition and spore germination inhibition assays. Among the plant extracts tested, 100% mycelial inhibition was observed for the methanol extract of Syzygium aromaticum at all three concentrations (2000 ppm, 3000 ppm and 4000 ppm), followed by the methanol extract of Inula racemosa (90.33%) at 4000 ppm. A maximum conidial germination inhibition of 83.54% was exhibited by the Heracleum candicans leaf extract. Phytochemical profiling of Syzygium aromaticum and Inula racemosa through liquid chromatography and mass spectrometry (HR-LCMS) revealed the presence of several compounds, such as eugenol, ursolic acid, quercetin, chlorogenic acid, and noscapine. A molecular docking approach was used to identify key inhibitory molecules against B. oryzae. Among the compounds detected in S. aromaticum and Inula racemosa, ursolic acid and noscapine were found to have the greatest binding affinity for the Big Mitogen Activated Protein Kinase (BMK-1) enzyme present in B. oryzae. In conclusion, S. aromaticum and Inula racemosa are potent compounds that could serve as lead compounds for drug discovery in the future.

Silver nanoparticles as potential fungicide against rice brown spot: physiological and biochemical responses in plants

Silver nanoparticles as potential fungicide against rice brown spot: physiological and biochemical responses in plants

Isolation and Molecular Characterization of Rice Brown Spot Disease in Major Cauvery Delta Regions

Rice (Oryza sativa L.) is the second most important staple food among agricultural cereal crops, with over half of the world's population dependent on rice. 165.59 million hectares of land is used for rice production worldwide, yielding 518.1 million tonnes and a productivity of 4.68 t/ha (FAOSTAT 2022 - 2023). In India, the rice production is about 132 million tonnes which is produced in about 47 million hectares of land with a productivity of about 4.21 t/ha (Ministry of Agriculture & Farmers welfare, Government of India 2023). In Tamil Nadu, the rice production is about 6.8 million metric tonnes which is produced in about 2.21 million hectares of land with a productivity of about 3.76 t/ha (Department of Economics and Statistics, Government of Tamil Nadu 2022-23). One of the biotic stresses that accounts for about 15.6% of losses each year is rice disease. Of them, Bipolaris oryzae (Breda de haan) causing brown spots of rice is the most destructive disease which causes about 90% of yield loss. Ten isolates of brown spot pathogen were collected from the Cauvery delta regions and of them, the virulent isolate (Bo9) was characterized molecularly. All the isolates were studied based on their morphological characteristics. ITS primers were utilized to do the molecular characterization of the pathogen.

Field biocontrol efficacy of Trichoderma spp. in fresh and dry formulations against rice blast and brown spot diseases and yield effect

Field biocontrol efficacy of Trichoderma spp. in fresh and dry formulations against rice blast and brown spot diseases and yield effect

Efficiency of bacterial isolates on suppressing brown spot in rice plants

AbstractMany fungicides can control brown spot, caused by Bipolaris oryzae in rice (Oryza sativa L.), although searching for beneficial microorganisms for biocontrol will improve sustainable integrated pest management. The aim of this investigation was the selection of natural suppressors of the brown spot disease in rice. We performed biochemical (E1), molecular antagonism (E2) and severity suppression (E3) studies under laboratory and greenhouse conditions. The E1, E2 and E3 assays were performed with 21 isolates. The experimental design was completely randomized with three replicates. E1 characterized all 21 isolates for the production of extracellular enzymes, siderophores, biofilm, indole‐3‐acetic acid (IAA) and phosphate solubilization. E2 was conducted to test the in vitro antagonism between the 21 isolates and B. oryzae, and E3 was conducted under greenhouse conditions, where seeds of the rice cultivars BRS‐Primavera and Esmeralda were sown in plastic trays containing 3 kg of fertilized soil. After 45 days, rice leaves were sprayed and inoculated with a mixed suspension of bacterial cells (1 × 108 CFU) and B. oryzae. All of the 21 bacterial isolates produced lipase, N‐fixation, siderophore, IAA, biofilm and solubilized phosphorus. Bacillus cereus, Priestia megaterium, Stenotrophomonas nitritireducens, Serratia marcescens and Serratia nematodiphila reduced radial colony growth of B. oryzae and suppressed brown spot severity by up to 90%. This indicates the potential of these isolates for biocontrol of brown spot in rice as a component of rice disease management.

IoT Based Wireless Communication System for Smart Irrigation and Rice Leaf Disease Prediction Using ResNeXt-50

Agriculture not only plays a vital role in human survival but also contributes to the nation’s greater economic development. With the use of technologies like IoT, WSNs, remote sensing, camera surveillance, and many more, precision agriculture is the newest buzzword in the field of technology. Its primary goal is to lessen the labour of farmers while increasing the output of farms. Many machine learning techniques are designed to improve the productivity and quality of the crops, but the improper irrigation and disease prediction of the existing techniques leads to loss of productivity and quality. Hence, the IoT based wireless communication system is designed for smart irrigation and rice leaf prediction using ANN and ResNeXt-50 model. In this designed model, smart irrigation is controlled by collecting the temperature and moisture of the soil in the agricultural field by using the WSN. Likewise, a surveillance camera is placed in the agricultural field to capture the rice leaf to find the disease such as rice blast, leaf smut, brown spot and bacterial blight. These collected data are processed and classified for smart irrigation and rice leaf disease prediction. For evaluating the performance of both the ANN and ResNeXt-50 trained model, the performance metrics such as accuracy, sensitivity, specificity, precision, error etc. The performance metrics for the ANN and ResNeXt-50 model are 0.9427, 0.925, 0.903, 0.86, 0.0573 and 0.967, 0.935, 0.943, 0.939 and 0.033. Thus, the evaluation of the designed model results that the proposed approach is performing better compared to the current techniques.

Performance analysis of various deep learning models for detecting rice diseases

<p>A major portion of the world’s population relies on rice as a staple diet, hence rice is essential to maintaining food security worldwide. Unfortunately, rice crops are susceptible to a number of illnesses that, if detected and treated promptly, can result in significant output losses. Expert visual inspection is a time-consuming and arbitrary part of the conventional procedures for diagnosing diseases in rice. An effective method for automated illness diagnosis in agriculture has evolved in recent years: deep learning, a branch of artificial intelligence. The objective of this research is to compare AlexNet, DCNN, MobileNet, GoogleNet, VGG16, ResNet50 and Xception, these are various deep learning models in order to choose the one that would produce the highest levels of accuracy, precision recall, specificity, and F1-score for detecting rice diseases. In this study we train the model for nine different types of rice diseases named as Rice Blast (Pyricularia oryzae), Rice Sheath Blight (Rhizoctonia Solani), Bacterial Leaf Blight (Xanthomonas oryzae pv. oryzae), Tungro Disease, Rice Grassy Stunt Virus (RGSV), Rice Yellow Mottle Virus (RYMV), Bakanae Disease (Fusarium moniliforme), Brown Spot (Cochliobolus miyabeanus) and Rice Tungro Bacilliform Virus (RTBV) with 30,000 images. For this we used the secondary dataset for analyzing the performance of models. We trained the model for both normalized and non-normalized dataset. After comparing the various models we get the better result from ResNet50 model with accuracy of 97.50%.</p>

Diagnosis of bacterial leaf blight, leaf smut, and brown spot in rice leafs using VGG16

In the context of agricultural sustainability and food security, timely and precise identification of plant diseases holds paramount significance. This research proposes an innovative approach to diagnose bacterial leaf blight (BLB), rice blast (RB), and brown spot (BS) in rice leaves utilizing the VGG16 convolutional neural network (CNN). By leveraging deep learning capabilities, the proposed model analyzes high-resolution images of rice leaves to classify and distinguish between the different diseases. The VGG16 architecture has shown remarkable performance in image recognition tasks, and this study aims to harness its potential for precision agriculture. Rice (Oryza sativa) is a staple crop that plays a crucial role in global food security. Timely and precise diagnosis of these diseases is vital for effective disease management and crop protection. Our methodology involves the collection and curation of a diverse dataset of rice leaf images. The VGG16 model, a proven deep convolutional neural network (CNN) architecture, is employed for extraction of features and classification. The results of our study demonstrate the efficacy of the VGG16 model in distinguishing between different rice leaf diseases with remarkable precision. This high level of accuracy holds great promise for the field of agriculture, offering a non-invasive and efficient means of disease detection. Early disease identification can facilitate timely intervention, reducing crop losses and mitigating the economic impact on farmers. The classification accuracy of the proposed method is 97.77% using the publicly available dataset.

Biological and Chemical Management Strategy to Control Brown Spot Disease in Rice Caused by Bipolaris oryzae

The main objective of this study was to assess the effectiveness of various fungicides in managing rice brown spot, a common fungal disease in India that can reduce crop yield and seed quality. During 2020-21 crop seasons, a research project was conducted in the experimental plot of the Department of Plant Pathology at the College of Agriculture in Rewa, Madhya Pradesh, India. Different treatments involving fungicide application at various rice growth stages were implemented. The results showed that all treatments involving fungicides significantly reduced the incidence of rice brown spot in-vitro condition and increased crop yield when compared to the control group. Among the fungicides, Difenoconazole 25% EC at a concentration of 200ppm proved to be highly effective in controlling the growth of the fungal mycelium. In in-vivo condition, the treatment involving T4, which includes seed treatment with the bio-control agent Trichoderma viride (10g/kg seeds), the application of Trichoderma viride (10g/l) at 15-20 days after transplanting, and the application of propiconazole (1g/l) at the booting exhibited the highest level of reduction in the incidence of brown spot disease, amounting to 13.6 per cent. The integrated management approach are very effective to control the brown spot disease in rice.

A brief study on rice diseases recognition and image classification: fusion deep belief network and S-particle swarm optimization algorithm

In the regions of southern Andhra Pradesh, rice brown spot, rice blast, and rice sheath blight have emerged as the most prevalent diseases. The goal of this research is to increase the precision and effectiveness of disease diagnosis by proposing a framework for the automated recognition and classification of rice diseases. Therefore, this work proposes a hybrid approach with multiple stages. Initially, the region of interest (ROI) is extracted from the dataset and test images. Then, the multiple features are extracted, such as color-moment-based features, grey-level cooccurrence matrix (GLCM)-based texture, and shape features. Then, the S-particle swarm optimization (SPSO) model selects the best features from the extracted features. Moreover, the deep belief network (DBN) model trained by SPSO is based on optimal features, which classify the different types of rice diseases. The SPSO algorithm also optimized the losses generated in the DBN model. The suggested model achieves a hit rate of 94.85% and an accuracy of 97.48% with the 10-fold cross-validation approach. The traditional machine learning (ML) model is significantly less accurate than the area under the receiver operating characteristic curve (AUC), which has an accuracy of 97.48%.

Genetic Analysis of Quantitative Traits, Gall Midge and Brown Spot Tolerance in Rice (Oryza sativa L.) Genotypes under Irrigated Ecosystem

Gall midge and brown spot of rice are a fungal disease that affect both seedlings and mature plants. To investigate resistance of rice to this disease twenty nine rice cultures were evaluated under irrigated ecosystem at Regional Agricultural Research Station, Jagtial during kharif, 2020. High heritable estimates were observed for all the 8 traits studied. High estimates of heritability was recorded for important yield attributes viz., days to 50% flowering (91.4, 11.24), plant height (77.8, 9.53), 1000-grain weight (98.4, 54.5), number of grains per panicle (78.3, 40.18) and grain yield (98.7, 33.87) indicated that these traits were under the control of additive genes. Significantly positive correlations were recorded with plant height, Number of productive tillers per plant and 1000 seed weight at both genotypic and phenotypic levels, the results clearly indicated that long duration genotypes with more height and bolder grains contribute for more grain yield. Among the eight traits studied, 1000 grain weight contributed highest (67.8%) towards total divergence followed by days to 50% flowering (11.5%), number of grains per panicle (6.06) and Galls midge incidence percentage (6.06%). Genotypes, JGL 38957 of cluster II, JGL 38950 of cluster III, JGL 38935 of cluster IV and JGL 38921 of Cluster VII could be the best source to develop brown spot, gall midge resistance and high yielding rice varieties.

Whale Optimized Deep Model for Paddy and Maize Leaf Disease Detection

Plant disease management is an essential process to minimize loss in the field of agriculture. Plant leaf disease detection(PLDD) technology helps the formers to reduce the loss of quality yield production. This study focuses on detecting paddy and maize plant leaf disease-detecting methods. It contributes to a PLDD method to improve detection accuracy and overall performance. The PLDD method introduced a whales-optimized artificial neural network (WOANN) method for classifying the four Maize and four rice leaf disease-related classes. The WOANN uses the whale optimizer's food-searching functionalities to improve the performance and detection accuracy of the dense net model. This WOANN classifies the maize leaf light, maize grey leaf spot, maize common rust, rice bacterial leaf blight, rice bacterial leaf steak, rice brown spot, and healthy leaves of both Maize and paddy. It uses the Hilbert-Schmidt independent criterion lasso correlation algorithm to support the WOANN classifier in selecting the significant features. The performance analysis shows that the WOANN-based approach achieves a maximum of 99.35% detection accuracy for maize leaf disease and 99.13% for paddy leaf disease. Its efficiency analysis shows that the WOANN-based approaches achieve a maximum accuracy rate than comparison approaches.

Isolation and Characterization of Rice Blast and Brown Spot Disease in Different Regions of Tamil Nadu, India

Rice (Oryza sativa L.) is the most important staple food crop cultivated in almost every district of Tamil Nadu. Among the rice fungal diseases, rice blast caused by Pyricularia oryzae and brown spot caused by Bipolaris oryzae are considered to be most potent threats causing major yield losses in Tamil Nadu. The present study aims in identification and characterization of rice blast and brown spot pathogen from different rice growing regions of Tamil Nadu. Three isolates of blast pathogen and five isolates of brown spot pathogen were collected and characterized based on colony morphology and conidial characters. P. oryzae and B. oryzae was identified based on cultural and morphological characters. Molecular characterization of blast and brown spot isolates were done with universal ITS primers. Pathogenicity test was carried out for all the isolates of blast and brown spot and the isolate showing maximum disease incidence was found as virulent one. The present work of morphological and molecular characterization of blast and brown spot pathogen will therefore be helpful to identify and manage the disease effectively in Tamil Nadu.

Detection of Bangladeshi-Produced Plant Disease Using a Transfer Learning Based on Deep Neural Model

Plant diseases pose a significant threat to agricultural productivity and food security in Bangladesh. In this research, we address the challenge of timely and accurate plant disease detection through the application of transfer learning with deep neural models. We curated a diverse dataset comprising 18 categories of plant leaf images, including Bell pepper Bacterial spot, Bell pepper Healthy, Peach Healthy, Potato Early Blight, Rice Leaf Blast, Rice Healthy, Rice Brown Spot, Potato Healthy, Peach Bacterial spot, Corn Blight, Potato Late blight, Corn Healthy, Tomato Bacterial spot, Strawberry Leaf Scorch, Tomato Early blight, Tomato Early blight, Strawberry Healthy, and Tomato Healthy. The dataset represents the most prevalent plant diseases observed in the Bangladeshi context. We employed three state-of-the-art deep learning algorithms, EfficientNetV2M, VGG-19, and NASNetLarge, to develop robust plant disease detection models. Through transfer learning, these pre-trained models were fine-tuned on our specialized dataset to adapt them for the task at hand. The performance evaluation revealed impressive results, with EfficientNetV2M achieving an accuracy rate of 99%, VGG-19 achieving 93%, and NASNetLarge attaining 83% accuracy. The high accuracy of EfficientNetV2M showcases its exceptional capability in accurately classifying plant diseases prevalent in Bangladesh. The success of these deep neural models in detecting various plant diseases signifies their potential in revolutionizing plant disease management and enhancing agricultural practices. Our research contributes valuable insights into the effective use of transfer learning for plant disease detection and emphasizes the significance of dataset curation for improved model performance. The developed models hold promise in providing timely and precise disease diagnosis to farmers and agricultural professionals, thereby facilitating prompt interventions and minimizing crop losses. Future research can explore the integration of these deep neural models into practical agricultural tools, enabling real-time disease detection and offering substantial benefits to the agricultural industry in Bangladesh.

Enhancing Rice Crop Management: Disease Classification Using Convolutional Neural Networks and Mobile Application Integration

Early diagnosis of rice disease is important because it poses a considerable threat to agricultural productivity as well as the global food security of the world. It is challenging to obtain more reliable outcomes based on the percentage of RGB value using image processing outcomes for rice disease detections and classifications in the agricultural field. Machine learning, especially with a Convolutional Neural Network (CNN), is a great tool to overcome this problem. But the utilization of deep learning techniques often necessitates high-performance computing devices, costly GPUs and extensive machine infrastructure. As a result, this significantly raises the overall expenses for users. Therefore, the demand for smaller CNN models becomes particularly pronounced, especially in embedded systems, robotics and mobile applications. These domains require real-time performance and minimal computational overhead, making smaller CNN models highly desirable due to their lower computational cost. This paper introduces a novel CNN architecture which is comparatively small in size and promising in performance to predict rice leaf disease with moderate accuracy and lower time complexity. The CNN network is trained with processed images. The image processing is performed using segmentation and k-means clustering to remove background and green parts of affected images. This technique proposes to detect rice disease of rice brown spot, rice bacterial blight and leaf smut with reliable outcomes in disease classifications. The model is trained using an augmented dataset of 2700 images (60% data) and validated with 1200 images of disease-affected samples to identify rice disease in agricultural fields. The model is tested with 630 images (14% data); testing accuracy is 97.9%. The model is exported into a mobile application to introduce the real-life application of the outcome of this work. The model accuracy is compared to others work associated with this type of problem. It is found that the performance of the model and the application are satisfactory compared to other works related to this work. The over-all accuracy is notable, showing the reliability and dependability of this model to classify rice leaf diseases.

Effect of rice rhizospheric Trichoderma spp and fungicide for the management of brown spot disease of rice

Brown spot of rice caused by Bipolaris oryzae is of worldwide occurrence and is known to cause substantial quantitative and qualitative losses in grain yield. This disease was responsible for the epic 'Great Bengal Famine' in 1942-1943, resulting in the death of 4 million people. Studies were carried out to evaluate the effect of rice rhizospheric Trichoderma spp and fungicide for the management of brown spot disease of rice. A total of 20 isolates of Trichoderma spp were isolated from rice rhizospheric soil and screened by dual culture method in which three isolates Ti20, Ti19 and Ti16 showing maximum inhibition of 67.86%, 64.29% and 60.71% were selected for further test. Six fungicides viz., Hexaconazole, Propiconazole, Carbendazim + Mancozeb, Metalaxyl + Mancozeb, Carbendazim and Copper oxychloride were evaluated against B. oryzae by poison food technique, out of which Propiconazole, Hexaconazole and Copper oxychloride were selected and further tested for their compatibility with the selected Trichoderma spp was done. The most efficient and compatible Trichoderma sp with fungicide i.e., (Ti19 + Hexaconazole 5% SC) were selected for managing the disease in pot experiment with different treatment combinations i.e., seed treatment, seedling dip and foliar spray. Results under pot experiment showed that, seed treatment with Ti19 and Hexaconazole 5% SC recorded lowest disease severity of 3.70%, 7.41% and 11.11% at tillering, booting and milking stages respectively, with significantly higher yield of 28.27 g/pot as compared to untreated control pots.

Transcriptome profiling uncovers the involvement of CmXyn1, a glycosyl hydrolase 11, in Cochliobolus miyabeanus pathogenicity

Necrotrophic pathogen Cochliobolus miyabeanus (C. miyabeanus) causes rice brown leaf spot disease and drastically affects the yield and quality of rice grains. However, the molecular mechanism of rice-C. miyabeanus remains poorly understood due to the limited research conducted on this pathosystem. To elucidate the molecular mechanism of rice-C. miyabeanus, a transcriptome analysis was conducted from in vitro and in planta grown C. miyabeanus. This analysis led to the identification of a total of 24,060 genes of which 426 in vitro and 57 in planta expressed genes were predicted to encode for secretory proteins. As these 57 genes were specifically expressed in planta and were predicted to be secretory in nature, these were consider as putative effectors, highlighting their possible roles in the fungal pathogenicity. Notably, among these putative effectors, CmXyn1 which encodes a glycosyl hydrolase 11 displayed the highest expression level under in planta conditions and was thus selected for further functional characterization. Interestingly, the extracellular expression of CmXyn1 transiently induced cell death in Nicotiana benthamiana leaves, while intracellular expression was comparatively lesser effective. In addition, transcriptome analysis on rice leaves during C. miyabeanus infection and comparing it to the rice leaf transcriptome data obtained during hemibiotrophic pathogen Magnaporthe oryzae infection led to the discovery of 18 receptors/receptor-like kinases that were commonly expressed in response to both pathogens, indicating their key roles in rice defense response. Taken together, our findings provide new insights into rice-C. miyabeanus interaction as well as the unique and common defense responses of rice against hemibiotroph and necrotroph model systems.

Effect of Soil and Climatic Conditions on Brown Spot Occurrence in Rice Lowland across Four Agro-climatic Zones of Côte d’Ivoire

Aims: To evaluate interaction of soil pH and acidity with weather on Rice Brown spot (BS) occurrence in rice lowlands.
 Study Design: Cross sectional study.
 Place and Duration of Study: Four distinct rice lowlands belonging to different climatic zones (forest, transitional and savanna) of Côte d’Ivoire during cropping seasons of 2021.
 Methodology: BS characterization were done in different farmer fields where soil samples were also collected during dry and rainy seasons. Soil silicon and acidity were determined in those samples and rice grain yield at harvest time were recorded in different sites. Weather data related to sites and seasons were used to find out correlations.
 Results: Occurrence of BS was found in forest zones with scores of 4 and 3 compared to 1 and 2 in savanna and transitional zones, respectively, with seasonal variation. Both rice production and the occurrence of BS were explained by soil parameters in conjunction with climatic parameters. Rainfall (R=0.38) and relative humidity (R=0.64) leaded BS occurrence and decrease of yield. Wind speed (R=0.62) and air maximum temperature (R= 0.63) were the determinant factors affecting rice yields. Si was found to be a component of sustainable soil management that interferes with soil pH in all climatic zones. Combined with Temperature, both soil parameters predicted BS occurrence over 50%.
 Conclusion: Temperature decrease BS pathogens occurrence whereas high humidity increases its spread. Those parameters combined with silicon which interferes with pH could leads sustainable solutions in BS control. Furthermore, having a deep understanding with rice varietal considerations can significantly improve strategies related to rice cultivation and protection.