Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Key Elements for Plant Nutrition

Key Elements for Plant Nutrition

Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...

Epiphytes, Parasites, and Carnivores

Epiphytes, Parasites, and Carnivores

Plants often form mutualistic relationships with soil-dwelling fungi or bacteria to enhance their roots’ nutrient uptake ability. Root-colonizing fungi (e.g., mycorrhizae) increase a plant’s root surface area, which promotes nutrient absorption. While root-colonizing, nitrogen-fixing bacteria (e.g., rhizobia) convert atmospheric nitrogen (N2) into ammonia (NH3), making nitrogen available to plants for various biological functions. For example, nitrogen is essential for the...

The Roles of Bacteria and Fungi in Plant Nutrition

The Roles of Bacteria and Fungi in Plant Nutrition

Plants have the impressive ability to create their own food through photosynthesis. However, plants often require assistance from organisms in the soil to acquire the nutrients they need to function correctly. Both bacteria and fungi have evolved symbiotic relationships with plants that help the species to thrive in a wide variety of environments.

Overview of Metabolism

Overview of Metabolism

Living cells constantly carry out various chemical reactions which are necessary for their proper functioning. These reactions are interlinked to one another via multiple pathways. The collection of these chemical reactions is known as metabolism.
Plant Metabolism
Sunlight, the primary source of energy in plants, is first absorbed by the chlorophyll pigments present in their leaves. Plants then use this energy to carry out photosynthesis, where water is oxidized into oxygen and carbon dioxide...

C4 Pathway and CAM

C4 Pathway and CAM

Most plants use the C3 pathway for carbon fixation. However, some plants, such as sugar cane, corn, and cacti that grow in hot conditions, use alternative pathways to fix carbon and conserve energy loss due to photorespiration. Photorespiration is the process that occurs when the oxygen concentration is high. Under such conditions, the rubisco enzyme in the Calvin cycle binds O2 instead of CO2, which halts photosynthesis and consumes energy.
C4 Pathway
The C4 pathway is used by plants such as...

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A robust stacked ensemble strategy with multi-optimizer CNN models for skin cancer classification.

Scientific reports·2026

Same author

A hybrid machine learning approach for automated malaria diagnosis from thin blood smear images.

Parasites & vectors·2026

Same author

Data-driven explainable chronic kidney disease detection using RF based data imputation and meta-ensemble learning.

Scientific reports·2026

Same author

Application of fractal theory in cancer detection: A review.

Bio Systems·2026

Same author

Hybrid deep learning model for air quality prediction and its impact on healthcare.

Scientific reports·2026

Same author

Automated DFU detection through GA-selected CNN ensemble with Grad-CAM interpretability.

Scientific reports·2025

Search research articles

Related Experiment Video

Updated: Jun 21, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

PND-Net: plant nutrition deficiency and disease classification using graph convolutional network.

Asish Bera¹, Debotosh Bhattacharjee^2,3, Ondrej Krejcar^3,4,5

¹Department of Computer Science and Information Systems, BITS Pilani, Pilani Campus, Pilani, Rajasthan, 333031, India. asish.bera@pilani.bits-pilani.ac.in.

Scientific Reports

|July 5, 2024

Summary

This summary is machine-generated.

Early detection of plant diseases and nutrient deficiencies using deep learning improves crop yields. A new Plant Nutrition Deficiency and Disease Network (PND-Net) combines convolutional neural networks (CNNs) with graph convolutional networks (GNNs) for accurate classification.

Keywords:

Agriculture Cancer classification Convolutional neural network Graph convolutional network Nutrition deficiency Plant disease Spatial pyramid pooling

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management

Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management

Published on: July 14, 2023

Related Experiment Videos

Last Updated: Jun 21, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management

Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management

Published on: July 14, 2023

Area of Science:

Agricultural Science
Computer Science
Biotechnology

Background:

Plant health monitoring is crucial for agricultural productivity and food security.
Early identification of plant diseases and nutritional deficiencies aids in timely intervention and yield enhancement.
Deep learning models have shown promise in automating the analysis of plant visual symptoms.

Purpose of the Study:

To propose a novel deep learning method, the Plant Nutrition Deficiency and Disease Network (PND-Net), for accurate classification of plant diseases and nutrient deficiencies.
To enhance classification accuracy by integrating regional feature learning with a graph convolutional network (GNN) upon a convolutional neural network (CNN) backbone.
To evaluate the generalization capabilities of PND-Net on both plant health and human medical imaging datasets.

Main Methods:

Developed PND-Net, a hybrid deep learning model combining CNNs for global feature extraction and GNNs for detailed regional analysis.
Employed spatial pyramidal pooling for multi-scale region-based feature summarization to capture discriminative features.
Evaluated PND-Net on public datasets for plant nutrition deficiency (Banana, Coffee) and plant disease classification (Potato, PlantDoc), using various CNN backbones.

Main Results:

PND-Net achieved high classification accuracies: 90.00% for Banana and 90.54% for Coffee nutrition deficiency.
Achieved 96.18% accuracy for Potato diseases and 84.30% for PlantDoc dataset using the Xception backbone.
Demonstrated state-of-the-art performance on medical imaging datasets (BreakHis, SIPaKMeD), indicating strong generalization.

Conclusions:

The proposed PND-Net effectively improves automated plant health analysis, contributing to agricultural growth.
The hybrid CNN-GNN approach with regional feature learning enhances classification accuracy for plant diseases and deficiencies.
PND-Net shows potential for diverse applications, including human cancer classification, highlighting its robustness.