ArticlePlants (Basel, Switzerland)2021
Classification of Plant Leaves Using New Compact Convolutional Neural Network Models.
Article in Plants (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 50 citations in OpenAlex.
- FloraMediX: AI-enabled recognition and therapeutic profiling analysis of indian medicinal flora.Frontiers in bioinformatics · 2026Article
- Hybrid Multi-Scale Neural Network with Attention-Based Fusion for Fruit Crop Disease Identification.Journal of imaging · 2025Article
- Article
- SeasVeg: An image dataset of Bangladeshi seasonal vegetables.Data in brief · 2024Article
- Application of artificial neural networks to classify Avena fatua and Avena sterilis based on seed traits: insights from European Avena populations primarily from the Balkan Region.BMC plant biology · 2024Article
- An improved deep learning network for image detection and its application in Dendrobii caulis decoction piece.Scientific reports · 2024Article
- Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting.Sensors (Basel, Switzerland) · 2022Article
- Intelligent Identification and Features Attribution of Saline-Alkali-Tolerant Rice Varieties Based on Raman Spectroscopy.Plants (Basel, Switzerland) · 2022Article
- YOLO-JD: A Deep Learning Network for Jute Diseases and Pests Detection from Images.Plants (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Precision crop safety relies on automated systems for detecting and classifying plants. This work proposes the detection and classification of nine species of plants of the PlantVillage dataset using the proposed developed compact convolutional neural networks and AlexNet with transfer learning. The models are trained using plant leaf data with different data augmentations. The data augmentation shows a significant improvement in classification accuracy. The proposed models are also used for the classification of 32 classes of the Flavia dataset. The proposed developed N1 model has a classification accuracy of 99.45%, N2 model has a classification accuracy of 99.65%, N3 model has a classification accuracy of 99.55%, and AlexNet has a classification accuracy of 99.73% for the PlantVillage dataset. In comparison to AlexNet, the proposed models are compact and need less training time. The proposed N1 model takes 34.58%, the proposed N2 model takes 18.25%, and the N3 model takes 20.23% less training time than AlexNet. The N1 model and N3 models are size 14.8 MB making it 92.67% compact, and the N2 model is 29.7 MB which makes it 85.29% compact as compared to AlexNet. The proposed models are giving good accuracy in classifying plant leaf, as well as diseases in tomato plant leaves.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.