AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection
- URL: http://arxiv.org/abs/2308.03766v1
- Date: Sun, 23 Jul 2023 19:58:40 GMT
- Title: AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection
- Authors: Anish Mall, Sanchit Kabra, Ankur Lhila and Pawan Ajmera
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This research paper presents AMaizeD: An End to End Pipeline for Automatic
Maize Disease Detection, an automated framework for early detection of diseases
in maize crops using multispectral imagery obtained from drones. A custom
hand-collected dataset focusing specifically on maize crops was meticulously
gathered by expert researchers and agronomists. The dataset encompasses a
diverse range of maize varieties, cultivation practices, and environmental
conditions, capturing various stages of maize growth and disease progression.
By leveraging multispectral imagery, the framework benefits from improved
spectral resolution and increased sensitivity to subtle changes in plant
health. 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. Experimental results
demonstrate the effectiveness of the framework in detecting a range of maize
diseases, including powdery mildew, anthracnose, and leaf blight. The framework
achieves state-of-the-art performance on the custom hand-collected dataset and
contributes to the field of automated disease detection in agriculture,
offering a practical solution for early identification of diseases in maize
crops advanced machine learning techniques and deep learning architectures.
Related papers
- Small data deep learning methodology for in-field disease detection [6.2747249113031325]
We present the first machine learning model capable of detecting mild symptoms of late blight in potato crops.
Our proposal exploits the availability of high-resolution images via the concept of patching, and is based on deep convolutional neural networks with a focal loss function.
Our model correctly detects all cases of late blight in the test dataset, demonstrating a high level of accuracy and effectiveness in identifying early symptoms.
arXiv Detail & Related papers (2024-09-25T17:31:17Z) - Multi-Class Plant Leaf Disease Detection: A CNN-based Approach with Mobile App Integration [0.0]
Plant diseases significantly impact agricultural productivity, resulting in economic losses and food insecurity.
This study investigates advanced techniques in plant disease detection, emphasizing the integration of image processing, machine learning, deep learning methods, and mobile technologies.
High-resolution images of plant leaves were captured and analyzed using convolutional neural networks (CNNs) to detect symptoms of various diseases, such as blight, mildew, and rust.
The model was trained on a diverse dataset encompassing multiple crops and disease types, achieving 98.14% accuracy in disease diagnosis.
arXiv Detail & Related papers (2024-08-26T07:16:41Z) - Self-supervised transformer-based pre-training method with General Plant Infection dataset [3.969851116372513]
This study proposes an advanced network architecture that combines Contrastive Learning and Masked Image Modeling (MIM)
The proposed network architecture demonstrates effectiveness in addressing plant pest and disease recognition tasks, achieving notable detection accuracy.
Our code and dataset will be publicly available to advance research in plant pest and disease recognition.
arXiv Detail & Related papers (2024-07-20T15:48:35Z) - Artificial Immune System of Secure Face Recognition Against Adversarial Attacks [67.31542713498627]
optimisation is required for insect production to realise its full potential.
This can be by targeted improvement of traits of interest through selective breeding.
This review combines knowledge from diverse disciplines, bridging the gap between animal breeding, quantitative genetics, evolutionary biology, and entomology.
arXiv Detail & Related papers (2024-06-26T07:50:58Z) - Generating Diverse Agricultural Data for Vision-Based Farming Applications [74.79409721178489]
This model is capable of simulating distinct growth stages of plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions.
Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture.
arXiv Detail & Related papers (2024-03-27T08:42:47Z) - 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) - 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) - 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) - A Multi-Plant Disease Diagnosis Method using Convolutional Neural
Network [0.0]
This chapter investigates an optimal plant disease identification model combining the diagnosis of multiple plants.
We implement numerous popular convolutional neural network (CNN) architectures.
The experimental results validate that the Xception and DenseNet architectures perform better in multi-label plant disease classification tasks.
arXiv Detail & Related papers (2020-11-10T15:18:52Z) - Pollen13K: A Large Scale Microscope Pollen Grain Image Dataset [63.05335933454068]
This work presents the first large-scale pollen grain image dataset, including more than 13 thousands objects.
The paper focuses on the employed data acquisition steps, which include aerobiological sampling, microscope image acquisition, object detection, segmentation and labelling.
arXiv Detail & Related papers (2020-07-09T10:33:31Z)
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.