8,703 research outputs found
Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning
Automatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. Deep learning, the latest breakthrough in computer vision, is promising for fine-grained disease severity classification, as the method avoids the labor-intensive feature engineering and threshold-based segmentation. Using the apple black rot images in the PlantVillage dataset, which are further annotated by botanists with four severity stages as ground truth, a series of deep convolutional neural networks are trained to diagnose the severity of the disease. The performances of shallow networks trained from scratch and deep models fine-tuned by transfer learning are evaluated systemically in this paper. The best model is the deep VGG16 model trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set. The proposed deep learning model may have great potential in disease control for modern agriculture
A system-theoretic approach for image-based infectious plant disease severity estimation
The demand for high level of safety and superior quality in agricultural products is of prime concern. The introduction of new technologies for supporting crop management allows the efficiency and quality of production to be improved and, at the same time, reduces the environmental impact. Common strategies to disease control are mainly oriented on spraying pesticides uniformly over cropping areas at different times during the growth cycle. Even though these methodologies can be effective, they present a negative impact in ecological and economic terms, introducing new pests and elevating resistance of the pathogens. Therefore, consideration for new automatic and accurate along with inexpensive and efficient techniques for the detection and severity estimation of pathogenic diseases before proper control measures can be suggested is of great realistic significance and may reduce the likelihood of an infection spreading. In this work, we present a novel system-theoretic approach for leaf image-based automatic quantitative assessment of pathogenic disease severity regardless of disease type. The proposed method is based on a highly efficient and noise-rejecting positive non-linear dynamical system that recursively transforms the leaf image until only the symptomatic disease patterns are left. The proposed system does not require any training to automatically discover the discriminative features. The experimental setup allowed to assess the system ability to generalise symptoms detection beyond any previously seen conditions achieving excellent results. The main advantage of the approach relies in the robustness when dealing with low-resolution and noisy images. Indeed, an essential issue related to digital image processing is to effectively reduce noise from an image whilst keeping its features intact. The impact of noise is effectively reduced and does not affect the final result allowing the proposed system to ensure a high accuracy and reliability
DETECTION OF PLANT LEAF DISEASES IN AGRICULTURE USING RECENT IMAGE PROCESSING TECHNIQUES – A REVIEW
Purpose: Agricultural productivity is something on which the economy highly depends in India as well in all over the world. India is an agriculture-dependent country; wherein about 70% of the population depends on agriculture.
Methodology: This is one of the main reasons that disease detection in agriculture plays an important role, as having the disease in plant leaf is quite natural. If proper observations are not taken in the agriculture field then it causes serious effects on plants due to which respective product quality and productivity are affected. Detection of plant leaf disease through effective and accurate automatic technique is beneficial at the starting stage as it reduces a large work of monitoring in big farms of crops.
Result: This paper presents the review on the state of the art disease classification techniques presently used using image processing that can be used for plant leaf disease detection in agriculture
Assessment of downy mildew in grapevine using computer vision and fuzzy logic. Development and validation of a new method
Downy mildew is a major disease of grapevine. Conventional methods for assessing crop
diseases are time-consuming and require trained personnel. This work aimed to develop and
validate a new method to automatically estimate the severity of downy mildew in grapevine
leaves using fuzzy logic and computer vision techniques. Leaf discs of two grapevine varieties
were inoculated with Plasmopara viticola and subsequently, RGB images were acquired under
indoor conditions. Computer vision techniques were applied for leaf disc location in Petri
dishes, image pre-processing and segmentation of pre-processed disc images to separate the
pixels representing downy mildew sporulation from the rest of the leaf. Fuzzy logic was applied
to improve the segmentation of disc images, rating pixels with a degree of infection according
to the intensity of sporulation. To validate the new method, the downy mildew severity was
visually evaluated by eleven experts and averaged score was used as the reference value. A
coefficient of determination (R2) of 0.87 and a root mean squared error (RMSE) of 7.61 %
was observed between the downy mildew severity obtained by the new method and the visual
assessment values. Classification of the severity of the infection into three levels was also
attempted, achieving an accuracy of 86 % and an F1 score of 0.78. These results indicate that
computer vision and fuzzy logic can be used to automatically estimate the severity of downy
mildew in grapevine leaves. A new method has been developed and validated to assess the
severity of downy mildew in grapevine. The new method can be adapted to assess the severity
of other diseases and crops in agriculture.European Commission 828940Spanish Government PID2020-119478GB-I00Universidad de La Rioja 1150/2020
Gobierno de La Rioj
SSM-Net for Plants Disease Identification in Low Data Regime
Plant disease detection is an essential factor in increasing agricultural
production. Due to the difficulty of disease detection, farmers spray various
pesticides on their crops to protect them, causing great harm to crop growth
and food standards. Deep learning can offer critical aid in detecting such
diseases. However, it is highly inconvenient to collect a large volume of data
on all forms of the diseases afflicting a specific plant species. In this
paper, we propose a new metrics-based few-shot learning SSM net architecture,
which consists of stacked siamese and matching network components to address
the problem of disease detection in low data regimes. We demonstrated our
experiments on two datasets: mini-leaves diseases and sugarcane diseases
dataset. We have showcased that the SSM-Net approach can achieve better
decision boundaries with an accuracy of 92.7% on the mini-leaves dataset and
94.3% on the sugarcane dataset. The accuracy increased by ~10% and ~5%
respectively, compared to the widely used VGG16 transfer learning approach.
Furthermore, we attained F1 score of 0.90 using SSM Net on the sugarcane
dataset and 0.91 on the mini-leaves dataset. Our code implementation is
available on Github: https://github.com/shruti-jadon/PlantsDiseaseDetection.Comment: 5 pages, 7 Figure
Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine
Plant diseases and pests cause a large loss of world agricultural production. Downy mildew is a major disease in grapevine. Conventional techniques for plant diseases evaluations are
time-consuming and require expert personnel. This work investigates novel sensing technologies
and artificial intelligence applications for assessing downy mildew in grapevine under laboratory
conditions. In our methodology, machine vision is applied to assess downy mildew sporulation,
while hyperspectral imaging is used to explore its potential capability towards early detection of
this disease. Image analysis applied to RGB leaf disc images is used to estimate downy mildew
(Plamopara viticola) severity in grapevine (Vitis vinifera L. cv Tempranillo). A determination coefficient (R2) of 0.76 ** and a root mean square error (RMSE) of 20.53% are observed in the correlation
between downy mildew severity by computer vision and expert’s visual assessment. Furthermore,
an accuracy of 81% is achieved to detect downy mildew early using hyperspectral images. These
results indicate that non-invasive sensing technologies and computer vision can be applied for assessing and quantify sporulation of downy mildew in grapevine leaves. The severity of this key
disease is evaluated in grapevine under laboratory conditions. In conclusion, computer vision, hyperspectral imaging and machine learning could be applied for important disease detection in
grapevine.Project NoPest (Novel Pesticides for a Sustainable Agriculture
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