Related papers: Image-based phenotyping of diverse Rice (Oryza Sativa L.) Genotypes

Image-based phenotyping of diverse Rice (Oryza Sativa L.) Genotypes

URL: http://arxiv.org/abs/2004.02498v1
Date: Mon, 6 Apr 2020 09:04:14 GMT
Title: Image-based phenotyping of diverse Rice (Oryza Sativa L.) Genotypes
Authors: Mukesh Kumar Vishal, Dipesh Tamboli, Abhijeet Patil, Rohit Saluja, Biplab Banerjee, Amit Sethi, Dhandapani Raju, Sudhir Kumar, R N Sahoo, Viswanathan Chinnusamy, J Adinarayana
Abstract summary: The need for high yielding rice varieties is a prime concern for developing nations like India, China, and other Asian-African countries. A total of 150 genotypes were grown at High Throughput Plant Phenomics facility, Nanajimukh Plant Phenomics Centre, Indian Council of Agricultural Research-Indian Agricultural Research Institute, New Delhi. We trained You Only Look Once (YOLO) deep learning algorithm for leaves tips detection and to estimate the number of leaves in a plant.
Score: 11.592053428575854
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Development of either drought-resistant or drought-tolerant varieties in rice (Oryza sativa L.), especially for high yield in the context of climate change, is a crucial task across the world. The need for high yielding rice varieties is a prime concern for developing nations like India, China, and other Asian-African countries where rice is a primary staple food. The present investigation is carried out for discriminating drought tolerant, and susceptible genotypes. A total of 150 genotypes were grown under controlled conditions to evaluate at High Throughput Plant Phenomics facility, Nanaji Deshmukh Plant Phenomics Centre, Indian Council of Agricultural Research-Indian Agricultural Research Institute, New Delhi. A subset of 10 genotypes is taken out of 150 for the current investigation. To discriminate against the genotypes, we considered features such as the number of leaves per plant, the convex hull and convex hull area of a plant-convex hull formed by joining the tips of the leaves, the number of leaves per unit convex hull of a plant, canopy spread - vertical spread, and horizontal spread of a plant. We trained You Only Look Once (YOLO) deep learning algorithm for leaves tips detection and to estimate the number of leaves in a rice plant. With this proposed framework, we screened the genotypes based on selected traits. These genotypes were further grouped among different groupings of drought-tolerant and drought susceptible genotypes using the Ward method of clustering.

Related papers

Anticipatory Understanding of Resilient Agriculture to Climate [66.008020515555]
We present a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. We focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population.
arXiv Detail & Related papers (2024-11-07T22:29:05Z)
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)
BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions [30.27773980916216]
Agricultural production is facing severe challenges in the next decades induced by climate change and the need for sustainability. Advancements in field management through non-chemical weeding by robots in combination with monitoring of crops by autonomous unmanned aerial vehicles (UAVs) are helpful to address these challenges. The analysis of plant traits, called phenotyping, is an essential activity in plant breeding, it however involves a great amount of manual labor.
arXiv Detail & Related papers (2023-12-22T14:06:44Z)
Development of a Prototype Application for Rice Disease Detection Using Convolutional Neural Networks [0.0]
Rice is the number one staple food in the Philippines. Farmers are not familiar with the different types of rice leaf diseases that might compromise the entire rice crop. The need to address the common bacterial leaf blight in rice is a serious disease that can lead to reduced yields and even crop loss of up to 75%.
arXiv Detail & Related papers (2023-01-13T13:12:40Z)
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)
Rice Leaf Disease Classification and Detection Using YOLOv5 [8.627180519837657]
The main issue facing the agricultural industry is rice leaf disease. As farmers in any country do not have much knowledge about rice leaf disease, they cannot diagnose rice leaf disease properly. This article proposes a rice leaf disease classification and detection method based on YOLOv5 deep learning.
arXiv Detail & Related papers (2022-09-04T09:27:57Z)
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)
Improved Neural Network based Plant Diseases Identification [0.0]
The agriculture sector is essential for every country because it provides a basic income to a large number of people and food as well, which is a fundamental requirement to survive on this planet. Due to improper knowledge of plant diseases, farmers use fertilizers in excess, which ultimately degrade the quality of food. In today time, Image processing is used to recognize and catalog plant diseases using the lesion region of plant leaf.
arXiv Detail & Related papers (2021-01-01T11:49:56Z)
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)
Two-View Fine-grained Classification of Plant Species [66.75915278733197]
We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
arXiv Detail & Related papers (2020-05-18T21:57:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.