Plant Disease Detection Using Image Processing and Machine Learning
- URL: http://arxiv.org/abs/2106.10698v1
- Date: Sun, 20 Jun 2021 14:11:24 GMT
- Title: Plant Disease Detection Using Image Processing and Machine Learning
- Authors: Pranesh Kulkarni, Atharva Karwande, Tejas Kolhe, Soham Kamble, Akshay
Joshi, Medha Wyawahare
- Abstract summary: This paper proposes a smart and efficient technique for detection of crop disease which uses computer vision and machine learning techniques.
The proposed system is able to detect 20 different diseases of 5 common plants with 93% accuracy.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: One of the important and tedious task in agricultural practices is the
detection of the disease on crops. It requires huge time as well as skilled
labor. This paper proposes a smart and efficient technique for detection of
crop disease which uses computer vision and machine learning techniques. The
proposed system is able to detect 20 different diseases of 5 common plants with
93% accuracy.
Related papers
- AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection [0.0]
AMaizeD is an automated framework for early detection of diseases in maize crops using multispectral imagery obtained from drones.
The proposed framework employs a combination of convolutional neural networks (CNNs) as feature extractors and segmentation techniques to identify both the maize plants and their associated diseases.
arXiv Detail & Related papers (2023-07-23T19:58:40Z) - Detection of healthy and diseased crops in drone captured images using
Deep Learning [0.0]
Disruptions in the plant's normal state, caused by diseases, often interfere with essential plant activities.
We propose a deep learning-based approach for efficient detection of plant diseases using drone-captured imagery.
arXiv Detail & Related papers (2023-05-22T21:15:12Z) - RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful
Representation from X-Ray Images [38.65823547986758]
We present a novel method called region-guided masked image modeling (RGMIM) for learning meaningful representation from X-ray images.
When using the entire training set, RGMIM outperformed other comparable methods, achieving a 0.962 lung disease detection accuracy.
arXiv Detail & Related papers (2022-11-01T07:41:03Z) - Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping [59.0626764544669]
In this study, we use Deep Learning methods to semantically segment grapevine leaves images in order to develop an automated object detection system for leaf phenotyping.
Our work contributes to plant lifecycle monitoring through which dynamic traits such as growth and development can be captured and quantified.
arXiv Detail & Related papers (2022-10-24T14:37:09Z) - Explainable vision transformer enabled convolutional neural network for
plant disease identification: PlantXViT [11.623005206620498]
Plant diseases are the primary cause of crop losses globally, with an impact on the world economy.
In this study, a Vision Transformer enabled Convolutional Neural Network model called "PlantXViT" is proposed for plant disease identification.
The proposed model has a lightweight structure with only 0.8 million trainable parameters, which makes it suitable for IoT-based smart agriculture services.
arXiv Detail & Related papers (2022-07-16T12:05:06Z) - A workflow for segmenting soil and plant X-ray CT images with deep
learning in Googles Colaboratory [45.99558884106628]
We develop a modular workflow for applying convolutional neural networks to X-ray microCT images.
We show how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate.
arXiv Detail & Related papers (2022-03-18T00:47:32Z) - Unsupervised deep learning techniques for powdery mildew recognition
based on multispectral imaging [63.62764375279861]
This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves.
We focus on unsupervised deep learning techniques applied to multispectral imaging data.
We propose the use of autoencoder architectures to investigate two strategies for disease detection.
arXiv Detail & Related papers (2021-12-20T13:29:13Z) - Potato Crop Stress Identification in Aerial Images using Deep
Learning-based Object Detection [60.83360138070649]
The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks.
The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level.
Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
arXiv Detail & Related papers (2021-06-14T21:57:40Z) - One-Shot Learning with Triplet Loss for Vegetation Classification Tasks [45.82374977939355]
Triplet loss function is one of the options that can significantly improve the accuracy of the One-shot Learning tasks.
Starting from 2015, many projects use Siamese networks and this kind of loss for face recognition and object classification.
arXiv Detail & Related papers (2020-12-14T10:44:22Z) - Real-time Plant Health Assessment Via Implementing Cloud-based Scalable
Transfer Learning On AWS DeepLens [0.8714677279673736]
We propose a machine learning approach to detect and classify plant leaf disease.
We use scalable transfer learning on AWS SageMaker and importing it on AWS DeepLens for real-time practical usability.
Our experiments on extensive image data set of healthy and unhealthy leaves of fruits and vegetables showed an accuracy of 98.78% with a real-time diagnosis of plant leaves diseases.
arXiv Detail & Related papers (2020-09-09T05:23:34Z) - Plant Disease Detection from Images [0.0]
This research focuses on creating a deep learning model that detects the type of disease that affected the plant from the images of the leaves of the plants.
The model is created using transfer learning and is experimented with both resnet 34 and resnet 50 to demonstrate that discriminative learning gives better results.
arXiv Detail & Related papers (2020-03-05T02:17:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.