36,244 research outputs found
How useful is Active Learning for Image-based Plant Phenotyping?
Deep learning models have been successfully deployed for a diverse array of
image-based plant phenotyping applications including disease detection and
classification. However, successful deployment of supervised deep learning
models requires large amount of labeled data, which is a significant challenge
in plant science (and most biological) domains due to the inherent complexity.
Specifically, data annotation is costly, laborious, time consuming and needs
domain expertise for phenotyping tasks, especially for diseases. To overcome
this challenge, active learning algorithms have been proposed that reduce the
amount of labeling needed by deep learning models to achieve good predictive
performance. Active learning methods adaptively select samples to annotate
using an acquisition function to achieve maximum (classification) performance
under a fixed labeling budget. We report the performance of four different
active learning methods, (1) Deep Bayesian Active Learning (DBAL), (2) Entropy,
(3) Least Confidence, and (4) Coreset, with conventional random sampling-based
annotation for two different image-based classification datasets. The first
image dataset consists of soybean [Glycine max L. (Merr.)] leaves belonging to
eight different soybean stresses and a healthy class, and the second consists
of nine different weed species from the field. For a fixed labeling budget, we
observed that the classification performance of deep learning models with
active learning-based acquisition strategies is better than random
sampling-based acquisition for both datasets. The integration of active
learning strategies for data annotation can help mitigate labelling challenges
in the plant sciences applications particularly where deep domain knowledge is
required
TOMATO DISEASE DETECTION MODEL BASED ON DENSENET AND TRANSFER LEARNING
Plant diseases are a foremost risk to the safety of food. They have the potential to significantly reduce agricultural products quality and quantity. In agriculture sectors, it is the most prominent challenge to recognize plant diseases. In computer vision, the Convolutional Neural Network (CNN) produces good results when solving image classification tasks. For plant disease diagnosis, many deep learning architectures have been applied. This paper introduces a transfer learning based model for detecting tomato leaf diseases. This study proposes a model of DenseNet201 as a transfer learning-based model and CNN classifier. A comparison study between four deep learning models (VGG16, Inception V3, ResNet152V2 and DenseNet201) done in order to determine the best accuracy in using transfer learning in plant disease detection. The used images dataset contains 22930 photos of tomato leaves in 10 different classes, 9 disorders and one healthy class. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.84% and validation accuracy of 99.30%
Plant Disease Detection using Deep Learning in Banana and Sunflower
In recent years plant disease detection and classification is finding a lot of scope in the field of agriculture. The use of image pre-processing along with deep learning techniques is making the role of farmers easy in the process of plant leaf disease detection. In this paper we propose a deep learning technique, ResNet-50 for the identification and classification of leaf diseases mainly in banana and sunflower. Images for the training and testing purpose are collected by visiting the farms and from village dataset for normal, leaf spot, leaf blight, powdery mildew, bunchy top, sigatoka, panama wilt. Pre-processing is done to remove eliminate the noise in the image by converting the RGB input to HSV image. Binary pictures are retrieved to separate the diseased and unaffected portions based on the hue and saturation components. A clustering method is utilized to separate the diseased region from the normal portion and the background. Classification of the disease is carried out using ResNet-50 algorithm. The experimental results obtained are compared with CNN, machine learning algorithms like SVM, KNN, DT and Ensemble algorithm like RF and XG booster. The proposed algorithm provided maximum efficiency compared to other algorithms
Design and Implementation of Deep Learning Method for Disease Identification in Plant Leaf
In the whole agriculture plays a very important in country’s economic condition specially in Indian agriculture has a crucial role for raising the Indian economic structure and its level. India’s frequent changing climatic situation, various bacterial disease is much normal that drastically decreases the productivity of crop productivity. Most of the researcher is moving towards into this topic to find the early detection technique to identify the disease in small green leaves plants. A single, micro bacterial infectious disease can destroy all the agricultural small green leaves plants get damaged overnight and hence must be prevented and cured as earliest as possible so that agriculture production. In this research work, we had tried to developed a green small green leaves plants bacterial disease early detection system based on the deep learning network system which will detect the disease at very earlier state of symptoms observed. Deep learning technique is has various algorithms to detect the earliest stage of any of the procedural processing of any bacterial infections or disease. This paper consists of investigations and analysis of latest deep learning techniques. Initially we will explore the deep learning architecture, its various source of data and different types of image processing method that can be used for processing the images captured of leaf for data processing. Different DL architectures with various data visualization’s tools has recently developed to determine symptoms and classifications of different type of plant-based disease. We had observed some issue that was un identified in previous research work during our literature survey and their technique to resolve that issue in order to handle the functional auto-detection system for identifying the certain plant disease in the field where massive growth of green small green leaves plants production is mostly done. Recently various enhancement has been done in techniques in CNN (convolution neural network) that generates much accurate images classification of any object. Our research work is based on deep learning network that will observe and identifies the symptoms generated in leaflet of plant and identifies the type of bacterial infection in progress in that with the help of plant classification stated in the plant dataset. Our research work represents the implementation DCGAN and Hybrid Net Model using Deep learning algorithm for early-stage identification of green plant leaves disease in various environmental condition. Our result obtained shows that it has DCGAN accuracy 96.90% when compared withHybrid Net model disease detection methodologies
Diagnosis of Rice Diseases using Canny Edge K-means Clustering and Convolutional Neural Network based Transfer Learning
Recent breakthroughs in deep learning-based convolutional neural networks have significantly improved image categorization accuracy. Deep learning-based techniques for diagnosing illnesses from rice plant images have been created in this work, inspired by the realisation of CNNs in image classification. Smart monitoring technologies for the automatic identification of plant diseases are extremely beneficial to sustainable agriculture. Despite the fact that various mechanisms for plant disease categorization have been created in recent years, an inefficient technique based on evidence from picture samples is of concern for ground environments. In this study, an image processing technique for pre-processing and segmentation was used, as well as a multi-class convolutional neural network with transfer learning, to classify rice plant leaf diseases such as brown spot, hispa, leaf blast, and healthy class. The contaminated area was automatically separated from the healthy areas of the image using canny edge detection and k-means clustering, and the features were retrieved using the CNN model. In the experimental results, the CNN model without transfer learning is compared to the transfer learning model. VGGNet transfer learning is used to construct a multi-classification framework for each class of rice illness. The overall accuracy acquired by the CNN model without transfer learning is 92.14%, whereas the accuracy obtained by the transfer learning model is 94.80%.The current work demonstrates that the proposed technique is compelling and capable of recognizing rice plant illness for four classes
A survey on different plant diseases detection using machine learning techniques
Early detection and identification of plant diseases from leaf images using machine learning is an important and challenging research area in the field of agriculture. There is a need for such kinds of research studies in India because agriculture is one of the main sources of income which contributes seventeen percent of the total gross domestic product (GDP). Effective and improved crop products can increase the farmer's profit as well as the economy of the country. In this paper, a comprehensive review of the different research works carried out in the field of plant disease detection using both state-of-art, handcrafted-features- and deep-learning-based techniques are presented. We address the challenges faced in the identification of plant diseases using handcrafted-features-based approaches. The application of deep-learning-based approaches overcomes the challenges faced in handcrafted-features-based approaches. This survey provides the research improvement in the identification of plant diseases from handcrafted-features-based to deep-learning-based models. We report that deep-learning-based approaches achieve significant accuracy rates on a particular dataset, but the performance of the model may be decreased significantly when the system is tested on field image condition or on different datasets. Among the deep learning models, deep learning with an inception layer such as GoogleNet and InceptionV3 have better ability to extract the features and produce higher performance results. We also address some of the challenges that are needed to be solved to identify the plant diseases effectively.Web of Science1117art. no. 264
Detection of healthy and diseased crops in drone captured images using Deep Learning
Monitoring plant health is crucial for maintaining agricultural productivity
and food safety. Disruptions in the plant's normal state, caused by diseases,
often interfere with essential plant activities, and timely detection of these
diseases can significantly mitigate crop loss. In this study, we propose a deep
learning-based approach for efficient detection of plant diseases using
drone-captured imagery. A comprehensive database of various plant species,
exhibiting numerous diseases, was compiled from the Internet and utilized as
the training and test dataset. A Convolutional Neural Network (CNN), renowned
for its performance in image classification tasks, was employed as our primary
predictive model. The CNN model, trained on this rich dataset, demonstrated
superior proficiency in crop disease categorization and detection, even under
challenging imaging conditions. For field implementation, we deployed a
prototype drone model equipped with a high-resolution camera for live
monitoring of extensive agricultural fields. The captured images served as the
input for our trained model, enabling real-time identification of healthy and
diseased plants. Our approach promises an efficient and scalable solution for
improving crop health monitoring systems
Benchmarking Self-Supervised Contrastive Learning Methods for Image-based Plant Phenotyping
Image-based plant phenotyping enables the high-throughput measurement of the physical characteristics of plants by combining
one or more imaging technologies with image analysis tools. Over the past decade, deep learning has been widely successful
for image-based tasks like image classification, object detection, image segmentation and object counting. While deep
learning has been applied to image-based plant phenotyping tasks like plant species classification, plant disease detection,
and leaf counting, its application has been limited. Part of the reason for this is that deep learning models tend to rely on large annotated datasets for training, and it can be expensive and time consuming to generate such datasets.
Motivated by the need to leverage unlabelled data, a lot of research effort has recently been directed towards the area of self-supervised learning (SSL). The common theme among various SSL methods is that they derive the supervisory signal
from the data itself, usually by distorting the input in some way and learning features that are invariant to the distortions. Despite the surge of research in this area, there has been a paucity of research applying self-supervised
learning on image-based plant phenotyping tasks, particularly detection and counting tasks. We address this gap by benchmarking two self-supervised learning methods -- MoCo v2 and DenseCL -- on four image-based plant phenotyping tasks
(the downstream tasks): wheat head detection, plant instance detection, wheat spikelet counting and leaf counting. We study the effects of the domain of the pre-training dataset on the transfer performance using four large-scale datasets: ImageNet
(general purpose concepts), iNaturalist 2021 (natural world images), iNaturalist 2021 Plants (plant images) and the TerraByte Field Crop datatset (crop images). To understand the differences between the internal representations of the neural networks trained with the different methods, we applied a representation similarity analysis technique known as orthogonal Procrustes distance. Our results show that (1) Finetuning a model that is pre-trained with an SSL method typically outperforms training
from scratch for a downstream task, (2) The Supervised pre-training method outperforms DenseCL and MoCo v2 for all the
downstream tasks, except for the leaf counting task where DenseCL excels, (3) There is not much difference, both in the downstream performance and the internal representations, between MoCo v2 and DenseCL pre-trained models, (4) Pre-training with the iNaturalist 2021 Plants dataset leads to the best downstream performance more often than other datasets, and
(5) Models pre-trained in a supervised manner learn more dissimilar features towards the last layers compared to models
pre-trained with MoCo v2 or DenseCL. We hope that this benchmark/evaluation study will inspire further studies towards the development of better self-supervised representation learning methods for image-based plant phenotyping tasks
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