An image classification approach to analyze the suppression of plant immunity by the human pathogen <it>Salmonella</it> Typhimurium

<p>Abstract</p> <p>Background</p> <p>The enteric pathogen <it>Salmonella</it> is the causative agent of the majority of food-borne bacterial poisonings. Resent research revealed that colonization of plants by <it>Salmonella</it> is an active infection process. <it>Salmonella</it> changes the metabolism and adjust the plant host by suppressing the defense mechanisms. In this report we developed an automatic algorithm to quantify the symptoms caused by <it>Salmonella</it> infection on <it>Arabidopsis</it>.</p> <p>Results</p> <p>The algorithm is designed to attribute image pixels into one of the two classes: healthy and unhealthy. The task is solved in three steps. First, we perform segmentation to divide the image into foreground and background. In the second step, a support vector machine (SVM) is applied to predict the class of each pixel belonging to the foreground. And finally, we do refinement by a neighborhood-check in order to omit all falsely classified pixels from the second step. The developed algorithm was tested on infection with the non-pathogenic <it>E. coli</it> and the plant pathogen <it>Pseudomonas syringae</it> and used to study the interaction between plants and <it>Salmonella</it> wild type and T3SS mutants. We proved that T3SS mutants of <it>Salmonella</it> are unable to suppress the plant defenses. Results obtained through the automatic analyses were further verified on biochemical and transcriptome levels.</p> <p>Conclusion</p> <p>This report presents an automatic pixel-based classification method for detecting “unhealthy” regions in leaf images. The proposed method was compared to existing method and showed a higher accuracy. We used this algorithm to study the impact of the human pathogenic bacterium <it>Salmonella</it> Typhimurium on plants immune system. The comparison between wild type bacteria and T3SS mutants showed similarity in the infection process in animals and in plants. Plant epidemiology is only one possible application of the proposed algorithm, it can be easily extended to other detection tasks, which also rely on color information, or even extended to other features.</p

Long Short-Term Memory Recurrent Neural Networks for Plant disease Identification

Farming profitability is something on which economy profoundly depends. This is the one reason that sickness recognition in plants assumes a critical job in farming field, as having infection in plants are very common. In the event that legitimate consideration isn't taken here, it causes genuine consequences for plants and because of which particular item quality, amount or profitability is influenced. This paper displays an algorithm for image segmentation technique which is utilized for automatic identification and classification plant leaf infections. It additionally covers review on various classification techniques that can be utilized for plant leaf ailment discovery. As the infected regions vary in length it is difficult to develop a feature vector of identical finite length representing all the sequences. A simple method to go around this issue is given by Recurrent Neural Networks (RNN). In this work we separate a feature vector through the use of Long Short-Term Memory (LSTM) recurrent neural networks. The LSTM network recursively repeats and concentrates two limited vectors whose link yields finite length vector portrayal

Drone Based Image Processing For Precision Agriculture

In today’s world, with an advent of technological advancements, the use of automated monitoring in agriculture is gaining increase in demand. In the agricultural field, yield loss occurs primarily due to widespread disease. Most of the disease is detected and identified when the disease progresses to a severe stage. A specially equipped UAV can perform several important tasks in agriculture, including monitoring the agriculture land and perform disease detection for several plants at an early stage. Currently, disease traits in agriculture are visually assessed, which can be time-consuming, less accurate and more subjective. Hence, in this project, image processing is used for the detection of plant disease. Detection of plant disease using automated image processing method is beneficial as it can reduce huge work of monitoring in big farms comprising of numerous crops. Moreover, in order to monitor big farms it is a viable option to use unmanned aerial vehicle on specific drone (UAV) to take the snap shots of various diseased plants from multiple angles. This study proposes a parallel image segmentation algorithm in order to detect the diseased leaf in Coconut, Palm, Banana, Dwarf Palmetto and Sapodilla plants acquire using Parrot PF728000 Anafi Drone with 4K HDR Camera. At first, the parallel K-means clustering algorithm was applied on the acquired image to segregate various components acquired using UAV. Post K-means clustering, the diseased portions of the plants were assessed using Hue-Saturation-Value (HSV) based image segmentation algorithm. Moreover, a comparison for image segmentation was also done on non-K-means clustered image and K-means clustered image for which a difference of 1839