Automated Plant Disease Recognition using Tasmanian Devil Optimization with Deep Learning Model

Plant diseases have devastating effects on crop production, contributing to major economic loss and food scarcity. Timely and accurate recognition of plant ailments is vital to effectual disease management and keeping further spread. Plant disease classification utilizing Deep Learning (DL) has gained important attention recently because of its potential to correct and affect the detection of plant diseases. DL approaches, particularly Convolutional Neural Networks (CNNs) demonstrate that extremely effective in capturing intricate patterns and features in plant leaf images, allowing correct disease classification. In this article, a Tasmanian Devil Optimization with Deep Learning Enabled Plant Disease Recognition (TDODL-PDR) technique is proposed for effective crop management. The TDODL-PDR technique derives feature vectors utilizing the Multi-Direction and Location Distribution of Pixels in Trend Structure (MDLDPTS) descriptor. Besides, the deep Bidirectional Long Short-Term Memory (BiLSTM) approach gets exploited for the plant disease recognition. Finally, the TDO method can be executed to optimize the hyperparameters of the BiLSTM approach. The TDO method inspired by the foraging behaviour of Tasmanian Devils (TDs) effectively explores the parameter space and improves the model's performance. The experimental values stated that the TDODL-PDR model successfully distinguishes healthy plants from diseased ones and accurately classifies different disease types. The automated TDODL-PDR model offers a practical and reliable solution for early disease detection in crops, enabling farmers to take prompt actions to mitigate the spread and minimize crop losses

Plant recognition, detection, and counting with deep learning

In agricultural and farm management, plant recognition, plant detection, and plant counting systems are crucial. We can apply these tasks to several applications, for example, plant disease detection, weed detection, fruit harvest system, and plant species identification. Plants can be identified by looking at their most discriminating parts, such as a leaf, fruit, flower, bark, and the overall plant, by considering attributes as shape, size, or color. However, the identification of plant species from field observation can be complicated, time-consuming, and requires specialized expertise. Computer vision and machine-learning techniques have become ubiquitous and are invaluable to overcome problems with plant recognition in research. Although these techniques have been of great help, image-based plant recognition is still a challenge. There are several obstacles, such as considerable species diversity, intra-class dissimilarity, inter-class similarity, and blurred resource images. Recently, the emerging of deep learning has brought substantial advances in image classification. Deep learning architectures can learn from images and notably increase their predictive accuracy. This thesis provides various techniques, including data augmentation and classification schemes, to improve plant recognition, plant detection, and plant counting system

Comparative analysis of plant immune receptor architectures uncovers host proteins likely targeted by pathogens.

BACKGROUND: Plants deploy immune receptors to detect pathogen-derived molecules and initiate defense responses. Intracellular plant immune receptors called nucleotide-binding leucine-rich repeat (NLR) proteins contain a central nucleotide-binding (NB) domain followed by a series of leucine-rich repeats (LRRs), and are key initiators of plant defense responses. However, recent studies demonstrated that NLRs with non-canonical domain architectures play an important role in plant immunity. These composite immune receptors are thought to arise from fusions between NLRs and additional domains that serve as "baits" for the pathogen-derived effector proteins, thus enabling pathogen recognition. Several names have been proposed to describe these proteins, including "integrated decoys" and "integrated sensors". We adopt and argue for "integrated domains" or NLR-IDs, which describes the product of the fusion without assigning a universal mode of action. RESULTS: We have scanned available plant genome sequences for the full spectrum of NLR-IDs to evaluate the diversity of integrations of potential sensor/decoy domains across flowering plants, including 19 crop species. We manually curated wheat and brassicas and experimentally validated a subset of NLR-IDs in wild and cultivated wheat varieties. We have examined NLR fusions that occur in multiple plant families and identified that some domains show re-occurring integration across lineages. Domains fused to NLRs overlap with previously identified pathogen targets confirming that they act as baits for the pathogen. While some of the integrated domains have been previously implicated in disease resistance, others provide new targets for engineering durable resistance to plant pathogens. CONCLUSIONS: We have built a robust reproducible pipeline for detecting variable domain architectures in plant immune receptors across species. We hypothesize that NLR-IDs that we revealed provide clues to the host proteins targeted by pathogens, and that this information can be deployed to discover new sources of disease resistance

An Outlook on the Localisation and Structure-Function Relationships of R Proteins in Solanum

The co-evolution of plants and plant-pathogens shaped a multi-layered defence system in plants, in which Resistance proteins (R proteins) play a significant role. A fundamental understanding of the functioning of these R proteins and their position in the broader defence system of the plant is essential. Sub-project 3 of the BIOEXPLOIT programme studies how R proteins are activated upon effector recognition and how recognition is conveyed in resistance signalling pathways, using the solanaceous R proteins Rx1 (from S. tuberosum spp. andigena; conferring extreme resistance against Potato Virus X), I-2 (from S. lycopersicon; mediating resistance to Fusarium oxysporum) and Mi-1.2 (from S. lycopersicon; conferring resistance to Meloidogyne incognita) as model systems. The results obtained in this project will serve as a model for other R proteins and will be translated to potential applications or alternative strategies for disease resistance. These include the modification of the recognition specificity of R proteins with the aim to obtain broad spectrum resistance to major pathogens in potato

Designing and Developing Smart Plant Information System

Pandemic COVID-19 have impacted the way of life of all people in the world. The effect continues worsened when our government-imposed lockdowns and every people need to stay home. Due to too long of staying at home, boredom become  one of the most reported negative psychological effects of the quarantine. It is undeniable that people of all ages enjoy gardening and planting. Hence, this pandemic time can be a golden opportunity for people to get outside of their compound and grow their interest in gardening. Thus, this project is designed to introduce the basic knowledge of plantations in easier and effective way. The system is applicable for multiple users who tend to learn more about plants and its plantation methods. Both men and women can start their own garden with proper planting methods. This system has been developed using several modules such as the leaf recognition using convolutional neural network (CNN), plant disease advice using knowledge based expert system and plant information using chatbot. The leaf recognition using CNN module will help the user to recognize a plant by providing the plant’s leaf image. The plant disease advice will diagnose the certain disease a plant might have by calculating the confidence level of provided symptoms of the disease. Finally, the plant information using chatbot module will provide information about plants by answering the user’s questions. It is hoped that this system could serve as the best smart plant information system to the user

Plant Disease Diagnosing Based on Deep Learning Techniques: A Survey and Research Challenges

Agriculture crops are highly significant for the sustenance of human life and act as an essential source for national income development worldwide. Plant diseases and pests are considered one of the most imperative factors influencing food production, quality, and minimize losses in production. Farmers are currently facing difficulty in identifying various plant diseases and pests, which are important to prevent plant diseases effectively in a complicated environment. The recent development of deep learning techniques has found use in the diagnosis of plant diseases and pests, providing a robust tool with highly accurate results. In this context, this paper presents a comprehensive review of the literature that aims to identify the state of the art of the use of convolutional neural networks (CNNs) in the process of diagnosing and identification of plant pest and diseases. In addition, it presents some issues that are facing the models performance, and also indicates gaps that should be addressed in the future. In this regard, we review studies with various methods that addressed plant disease detection, dataset characteristics, the crops, and pathogens. Moreover, it discusses the commonly employed five-step methodology for plant disease recognition, involving data acquisition, preprocessing, segmentation, feature extraction, and classification. It discusses various deep learning architecture-based solutions that have a faster convergence rate of plant disease recognition. From this review, it is possible to understand the innovative trends regarding the use of CNN’s algorithms in the plant diseases diagnosis and to recognize the gaps that need the attention of the research community

Optimierung der auf Deep Learning basierenden Klassifizierung von Pflanzenkrankheiten mit CBAM

In recent years, deep learning-based plant disease classification has been widely developed. However, it is challenging to collect sufficient annotated image data to effectively train deep learning models for plant disease recognition. The attention mechanism in deep learning assists the model to focus on the informative data segments and extract the discriminative features of inputs to enhance training performance. This paper investigates the Convolutional Block Attention Module (CBAM) to improve classification with CNNs, which is a lightweight attention module that can be plugged into any CNN architecture with negligible overhead. Specifically, CBAM is applied to the output feature map of CNNs to highlight important local regions and extract more discriminative features. Well-known CNN models (i.e. EfficientNetB0, MobileNetV2, ResNet50, InceptionV3, and VGG19) were applied to do transfer learning for plant disease classification and then fine-tuned by a publicly available plant disease dataset of foliar diseases in pear trees called DiaMOS Plant. Amongst others, this dataset contains 3006 images of leaves affected by different stress symptoms. Among the tested CNNs, EfficientNetB0 has shown the best performance. EfficientNetB0+CBAM has outperformed EfficientNetB0 and obtained 86.89% classification accuracy. Experimental results show the effectiveness of the attention mechanism to improve the recognition accuracy of pre-trained CNNs when there are few training data

Plant pathology: Many roads lead to resistance

Recent studies suggest that plant disease-resistance responses use multiple signaling pathways acting subsequent to pathogen recognition, and that phosphorylation cascades play a prominent role in the recognition and execution of foreign invaders