Priority selection of agro-meteorological parameters for integrated plant diseases management through analytical hierarchy process

To understand the influence of agro-meteorological parameters to take decisions related to various factors in an integrated plant disease management, it becomes vital to carry out scientific studies on the factors affecting it. The different agro-meteorological parameters namely temperature, humidity, moisture, rain, phenological week, cropping season, soil type, location, precipitation, heat index, and cloud coverage have been considered for this study. Each parameter has been allocated the ranking by using a technique called analytical hierarchical process (AHP). The parameter priorities are determined by calculating the Eigenvalues. This helps to make decisions related to integrated plant disease management where the prediction of plant disease occurrence, yield prediction, irrigation requirements, and fertilization recommendations can be taken. To take these decisions which parameters are good indicators can be identified using this method. The parameters majorly contribute to plant diseases and pest management decision making while delivers minor contribution in irrigation and fertilizer management related decision making. The manual results are compared with software generated results which indicates that both the results correlate with each other. Therefore, AHP technique can be successfully implemented for prioritizing agro-meteorological parameters for integrated plant diseases management as the results for both levels are consistent (consistency ratio < 0.1).

Spatio-Temporal Modeling and Simulation of Asian Soybean Rust Based on Fuzzy System

Mato Grosso, Brazil, is the largest soy producer in the country. Asian Soy Rust is a disease that has already caused a lot of damage to Brazilian agribusiness. The plant matures prematurely, hindering the filling of the pod, drastically reducing productivity. It is caused by the Phakopsora pachyrhizi fungus. For a plant disease to establish itself, the presence of a pathogen, a susceptible plant, and favorable environmental conditions are necessary. This research developed a fuzzy system gathering these three variables as inputs, having as an output the vulnerability of the region to the disease. The presence of the pathogen was measured using a diffusion-advection equation appropriate to the problem. Some coefficients were based on the literature, others were measured by a fuzzy system and others were obtained by real data. From the mapping of producing properties, the locations where there are susceptible plants were established. And the favorable environmental conditions were also obtained from a fuzzy system, whose inputs were temperature and leaf wetness. Data provided by IBGE, INMET, and Antirust Consortium were used to fuel the model, and all treatments, tests, and simulations were carried out within the Matlab® environment. Although Asian Soybean Rust was the chosen disease here, the model was general in nature, so could be reproduced for any disease of plants with the same profile.

A Smartphone-based Plant Disease Detection and Treatment Recommendation System using Machine Learning Techniques

Plant diseases cause major crop production losses worldwide, and a lot of significant research effort has been directed toward making plant disease identification and treatment procedures more effective. It would be of great benefit to farmers to be able to utilize the current technology in order to leverage the challenges facing agricultural production and hence improve crop production and operation profitability. In this work, we designed and implemented a user-friendly smartphone-based plant disease detection and treatment recommendation system using machine learning (ML) techniques. CNN was used for feature extraction while the ANN and KNN were used to classify the plant diseases; a content-based filtering recommendation algorithm was used to suggest relevant treatments for the detected plant diseases after classification. The result of the implementation shows that the system correctly detected and recommended treatment for plant diseases

Characteristics of biological control and mechanisms of Pseudomonas chlororaphis zm-1 against peanut stem rot

Background Peanut stem rot is a serious plant disease that causes great economic losses. At present, there are no effective measures to prevent or control the occurrence of this plant disease. Biological control is one of the most promising plant disease control measures. In this study, Pseudomonas chlororaphis subsp. aurantiaca strain zm-1, a bacterial strain with potential biocontrol properties isolated by our team from the rhizosphere soil of Anemarrhena asphodeloides, was studied to control this plant disease. Methods We prepared extracts of Pseudomonas chloroaphis zm-1 extracellular antibacterial compounds (PECEs), determined their antifungal activities by confrontation assay, and identified their components by UPLC-MS/MS. The gene knockout strains were constructed by homologous recombination, and the biocontrol efficacy of P. chlororaphis zm-1 and its mutant strains were evaluated by pot experiments under greenhouse conditions and plot experiments, respectively. Results P. chlororaphis zm-1 could produce extracellular antifungal substances and inhibit the growth of Sclerotium rolfsii, the main pathogenic fungus causing peanut stem rot. The components of PECEs identified by UPLC-MS/MS showed that three kinds of phenazine compounds, i.e., 1-hydroxyphenazine, phenazine-1-carboxylic acid (PCA), and the core phenazine, were the principal components. In particular, 1-hydroxyphenazine produced by P. chlororaphis zm-1 showed antifungal activities against S. rolfsii, but 2-hydroxyphenazine did not. This is quite different with the previously reported. The extracellular compounds of two mutant strains, ΔphzH and ΔphzE, was analysed and showed that ΔphzE did not produce any phenazine compounds, and ΔphzH no longer produced 1-hydroxyphenazine but could still produce PCA and phenazine. Furthermore, the antagonistic ability of ΔphzH declined, and that of ΔphzE was almost completely abolished. According to the results of pot experiments under greenhouse conditions, the biocontrol efficacy of ΔphzH dramatically declined to 47.21% compared with that of wild-type P. chlororaphis zm-1 (75.63%). Moreover, ΔphzE almost completely lost its ability to inhibit S. rolfsii (its biocontrol efficacy was reduced to 6.19%). The results of the larger plot experiments were also consistent with these results. Conclusions P. chlororaphis zm-1 has the potential to prevent and control peanut stem rot disease. Phenazines produced and secreted by P. chlororaphis zm-1 play a key role in the control of peanut stem rot caused by S. rolfsii. These findings provide a new idea for the effective prevention and treatment of peanut stem rot.

DeepLRR: An Online Webserver for Leucine-Rich-Repeat Containing Protein Characterization Based on Deep Learning

Members of the leucine-rich repeat (LRR) superfamily play critical roles in multiple biological processes. As the LRR unit sequence is highly variable, accurately predicting the number and location of LRR units in proteins is a highly challenging task in the field of bioinformatics. Existing methods still need to be improved, especially when it comes to similarity-based methods. We introduce our DeepLRR method based on a convolutional neural network (CNN) model and LRR features to predict the number and location of LRR units in proteins. We compared DeepLRR with six existing methods using a dataset containing 572 LRR proteins and it outperformed all of them when it comes to overall F1 score. In addition, DeepLRR has integrated identifying plant disease-resistance proteins (NLR, LRR-RLK, LRR-RLP) and non-canonical domains. With DeepLRR, 223, 191 and 183 LRR-RLK genes in Arabidopsis (Arabidopsis thaliana), rice (Oryza sativa ssp. Japonica) and tomato (Solanum lycopersicum) genomes were re-annotated, respectively. Chromosome mapping and gene cluster analysis revealed that 24.2% (54/223), 29.8% (57/191) and 16.9% (31/183) of LRR-RLK genes formed gene cluster structures in Arabidopsis, rice and tomato, respectively. Finally, we explored the evolutionary relationship and domain composition of LRR-RLK genes in each plant and distributions of known receptor and co-receptor pairs. This provides a new perspective for the identification of potential receptors and co-receptors.

A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion

Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision (CV) provides a great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage patterns in the disease progression. In this study, we proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, Weighted Distance Metric and the t-stochastic neighbor embedding algorithm (WDM-tSNE). To verify the effectiveness, we applied our method on four public datasets of images. The results demonstrated that the newly developed tool is capable of identifying the plant disease and further uncover the underlying subtypes associated with pathogenic resistance. In summary, the current framework provides great clustering performance for the root or leave images of diseased plants with pronounced disease spots or symptoms.

Plant Disease Detection and Monitoring Using Artificial Neural Network

Fungi have been identified as a major threat to crop production in the world. In this study, methods of improving the performance of plant disease detection and prediction using artificial neural network techniques are presented. The hyperspectral fungi dataset of 21 plant species were collected and trained using backpropagation algorithms of an artificial neural network to improve the conventional hyperspectral sensor. The system was modelled using self-defining equations and universal modelling diagrams and then implemented in the neural network toolbox in Matlab. The system was tested validated and the result showed a fungi detection accuracy of 96.61% and the percentage increment was 19.53%.