Numerical Modeling and Design of Machine Learning Based Paddy Leaf Disease Detection System for Agricultural Applications

In order to satisfy the insatiable need for ever more bountiful harvests on the global market, the majority of countries deploy cutting-edge technologies to increase agricultural output. Only the most cutting-edge technologies can ensure an appropriate pace of food production. Abiotic stress factors that can affect plants at any stage of development include insects, diseases, drought, nutrient deficiencies, and weeds. On the amount and quality of agricultural production, this has a minimal effect. Identification of plant diseases is therefore essential but challenging and complicated. Paddy leaves must thus be closely watched in order to assess their health and look for disease symptoms. The productivity and production of the post-harvest period are significantly impacted by these illnesses. To gauge the severity of plant disease in the past, only visual examination (bare eye observation) methods have been employed. The skill of the analyst doing this analysis is essential to the caliber of the outcomes. Due to the large growing area and need for ongoing human monitoring, visual crop inspection takes a long time. Therefore, a system is required to replace human inspection. In order to identify the kind and severity of plant disease, image processing techniques are used in agriculture. This dissertation goes into great length regarding the many ailments that may be detected in rice fields using image processing. Identification and classification of the four rice plant diseases bacterial blight, sheath rot, blast, and brown spot are important to enhance yield. The other communicable diseases, such as stem rot, leaf scald, red stripe, and false smut, are not discussed in this paper. Despite the increased accuracy they offer, the categorization and optimization strategies utilized in this work lead it to take longer than typical to finish. It was evident that employing SVM techniques enabled superior performance results, but at a cost of substantial effort. K-means clustering is used in this paper segmentation process, which makes figuring out the cluster size, or K-value, more challenging. This clustering method operates best when used with images that are comparable in size and brightness. However, when the images have complicated sizes and intensity values, clustering is not particularly effective

Watching plants grow:A position paper on computer vision and Arabidopsis thaliana

The authors present a comprehensive overview of image processing and analysis work done to support research into the model flowering plant Arabidopsis thaliana. Beside the plant's importance in biological research, using image analysis to obtain experimental measurements of it is an interesting vision problem in its own right, involving the segmentation and analysis of sequences of images of objects whose shape varies between individual specimens and also changes over time. While useful measurements can be obtained by segmenting a whole plant from the background, they suggest that the increased range and precision of measurements made available by leaf‐level segmentation makes this a problem well worth solving. A variety of approaches have been tried by biologists as well as computer vision researchers. This is an interdisciplinary area and the computer vision community has an important contribution to make. They suggest that there is a need for publicly available datasets with ground truth annotations to enable the evaluation of new approaches and to support the building of training data for modern data‐driven computer vision approaches, which are those most likely to result in the kind of fully automated systems that will be of use to biologists

A high-throughput screening system for barley/powdery mildew interactions based on automated analysis of light micrographs

<p>Abstract</p> <p>Background</p> <p>To find candidate genes that potentially influence the susceptibility or resistance of crop plants to powdery mildew fungi, an assay system based on transient-induced gene silencing (TIGS) as well as transient over-expression in single epidermal cells of barley has been developed. However, this system relies on quantitative microscopic analysis of the barley/powdery mildew interaction and will only become a high-throughput tool of phenomics upon automation of the most time-consuming steps.</p> <p>Results</p> <p>We have developed a high-throughput screening system based on a motorized microscope which evaluates the specimens fully automatically. A large-scale double-blind verification of the system showed an excellent agreement of manual and automated analysis and proved the system to work dependably. Furthermore, in a series of bombardment experiments an RNAi construct targeting the <it>Mlo </it>gene was included, which is expected to phenocopy resistance mediated by recessive loss-of-function alleles such as <it>mlo5</it>. In most cases, the automated analysis system recorded a shift towards resistance upon RNAi of <it>Mlo</it>, thus providing proof of concept for its usefulness in detecting gene-target effects.</p> <p>Conclusion</p> <p>Besides saving labor and enabling a screening of thousands of candidate genes, this system offers continuous operation of expensive laboratory equipment and provides a less subjective analysis as well as a complete and enduring documentation of the experimental raw data in terms of digital images. In general, it proves the concept of enabling available microscope hardware to handle challenging screening tasks fully automatically.</p

Computer Vision Techniques for Ambient Intelligence Applications

Ambient Intelligence (AmI) is a muldisciplinary area which refers to environments that are sensitive and responsive to the presence of people and objects. The rapid progress of technology and simultaneous reduction of hardware costs characterizing the recent years have enlarged the number of possible AmI applications, thus raising at the same time new research challenges. In particular, one important requirement in AmI is providing a proactive support to people in their everyday working and free-time activities. To this aim, Computer Vision represents a core research track since only through suitable vision devices and techniques it is possible to detect elements of interest and understand the occurring events. The goal of this thesis is presenting and demonstrating efficacy of novel machine vision research contributes for different AmI scenarios: object keypoints analysis for Augmented Reality purpose, segmentation of natural images for plant species recognition and heterogeneous people identification in unconstrained environments

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Perception of Groundnuts Leaf Disease by Neural Network with Progressive Re-Sizing

India is the world's second-largest groundnut producer after Brazil. An major crop of oilseeds is groundnuts. Because of this, the crop's quality and yield have declined, which has had a detrimental effect on the agricultural economy. This is partly because the crop is more susceptible to various diseases. It is required to create more precise and reliable automated approaches to address this problem and improve the identification of groundnut leaf diseases. This article proposes a deep learning-driven approach based on a progressive scaling technique for the accurate classification and identification of groundnut leaf diseases. The five main groundnut leaf diseases that are the subject of this study are leaf spot, armyworm effect, wilts, yellow leaf, and healthy leaf. The proposed model is trained using both progressive resizing and conventional techniques, and its performance is assessed using cross-entropy loss. A fresh dataset is meticulously curated in Gujarat state, India's Saurashtra region, for training and validation. Due to the dataset's uneven sample distribution across disease categories, an extended focus loss function was used to correct this class imbalance. In order to evaluate the performance of the suggested model, a number of performance metrics are utilized, including accuracy, sensitivity, F1-score, precision, and sensitivity. Notably, the suggested model has a 96.12% success rate, which signifies a considerable increase in the disease identification accuracy. It's important to note that the model incorporating progressive resizing beats the basic neural network-based model based on cross-entropy loss, highlighting the potency of the recommended approach

Tree species identification and leaf segmentation from natural images using deep semi-supervised learning

Thesis (MEng)--Stellenbosch University, 2022.ENGLISH ABSTRACT: Species identification is of significant importance to biodiversity conservation. However, there has been a sharp decline in expert species identification skills. This decline neces sitates automated tools for assisting accurate species identification. Earlier work on automated plant species classification focused on single plant at tributes with simple backgrounds. We advance automatic tree species identification by compiling a real-world natural image dataset for species identification. The multi-layered complexity of the dataset requires unconventional approaches for its utilisation. Deep semi-supervised learning (SSL) methods use labelled and additional unlabelled data for training a deep learning classifier. We present an SSL method for automated tree species identification from realistic, natural images. Our two-fold identification method exploits unlabelled images to perform tree feature recognition followed by species classi fication. The feature recognition step extracts bark and leaf images automatically from images with various tree features using minimal labelled data. We subsequently perform species classification of 50 chosen tree species and outperform traditional supervised learn ing (SL) approaches. Further, accurate image segmentation of leaves is critical for studying plant species characteristics. Current leaf segmentation algorithms are dependent on uniform leaf images or human interaction. Therefore, we propose an automated leaf segmentation method for extracting information from natural images. We employ our SSL feature recognition model for detection leaves and achieve state-of-the-art segmentation accuracy.AFRIKAANSE OPSOMMING: Spesie-identifikasie is van beduidende belang vir biodiversiteitsbewaring. Die skerp af name in spesie-identifikasievaardighede noodsaak geoutomatiseerde hulpmiddels om iden tifikasie te help. Vorige werk aan geoutomatiseerde plantspesieklassifikasie het hoofsaaklik gefokus op enkelplanteienskappe met eenvoudige agtergronde. Ons gebruik beelde van natuurlike instellings om outomatiese boomspesie-identifikasie te bevorder deur ’n werk like datastel vir spesie-identifikasie saam te stel. Om die veelvlakkige kompleksiteit van die datastel te benut, vereis onkonvensionele benaderings. Diep semi-toesig leer (SSL) metodes gebruik gemerkte en bykomende ongemerkte data vir die opleiding van ’n diep leer klassifiseerder. Ons bied ’n outomatiese SSL-metode vir boomspesie-identifikasie vanaf realistiese, natuurlike beelde aan. Ons tweevoudige identifikasiemetode ontgin ongemerkte natuurlike beelde om boomkenmerke te herken, gevolg deur spesieklassifikasie. Die kenmerkherkenningstap onttrek bas- en blaarbeelde outomaties uit beelde met verskeie boomkenmerke met minimale benoemde data. Ons voer vervolgens spesieklassifikasie van 50 gekose boomspesies uit en presteer beter as tradisionele toesigleer-benaderings (SL). Akkurate beeldsegmentering van blare is krities vir die bestudering van plantspesie eienskappe. Huidige blaarsegmenteringsalgoritmes is afhanklik van eenvormige blaar beelde of menslike interaksie. Daarom stel ons ’n outomatiese blaarsegmenteringsmetode voor om inligting uit natuurlike beelde te onttrek. Ons gebruik SSL-kenmerkenningsmodel vir opsporing van blare en bereik die nuutste segmentasie-akkuraatheid.Master

Plant Disease Detection and Classification by Deep Learning

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly

Presenting Visual Information to the User: Combining Computer Vision and Interface Design

In this work, we suggest better ways to present visual information (image databases) for browsing and retrieval. Thumbnails obtained from an image set give a good overview of its contents. Instead of simply downsampling images to obtain thumbnails, we first find salient regions (saliency map) using local statistical features of the image. We crop and downsample the images based on these saliency maps, and obtain better thumbnails. The suggested methods of finding salient regions are faster than existing methods while giving comparable results. Secondly, we have developed a Content Based Image Retrieval (CBIR) system to provide empirical evidence (by user study) that similarity based grouped and hierarchical placement of images is better than random placement. Using an effective shape based similarity measure we conclude that visual search is very useful in image retrieval systems. We conducted a field test to check the robustness of the system in varying photography conditions