Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Three pollination methods are commonly used in the greenhouse cultivation of tomato. These are pollination using insects, artificial pollination (by manually vibrating flowers), and plant growth regulators. Insect pollination is the preferred natural technique. We propose a new pollination method, using flower classification technology with Artificial Intelligence (AI) administered by drones or robots. To pollinate tomato flowers, drones or robots must recognize and classify flowers that are ready to be pollinated. Therefore, we created an AI image classification system using a machine learning convolutional neural network (CNN). A challenge is to successfully classify flowers while the drone or robot is constantly moving. For example, when the plant is shaking due to wind or vibration caused by the drones or robots. The AI classifier was based on an image analysis algorithm for pollination flower shape. The experiment was performed in a tomato greenhouse and aimed for an accuracy rate of at least 70% for sufficient pollination. The most suitable flower shape was confirmed by the fruiting rate. Tomato fruit with the best shape were formed by this method. Although we targeted tomatoes, the AI image classification technology is adaptable for cultivating other species for a smart agricultural future.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9905599 | PMC |
| http://dx.doi.org/10.1038/s41598-023-27971-z | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
First report on machine learning based multiclass classification of Caco-2 permeability using different balancing strategies.
SAR QSAR Environ Res
September 2025
Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India.
Evaluating the permeability of different molecular structures across the Caco-2 cell line is crucial for drug discovery and development. The present study primarily focuses on developing machine learning-based multiclass classification models for predicting the permeability of molecules across the Caco-2 cell line. However, the class imbalance in permeability datasets poses a significant challenge for developing predictive models in the case of multiclass analysis.
View Article and Find Full Text PDFInterpretable Machine Learning for Proteomics-Based Subtyping and Tumor Mutational Burden Prediction in Endometrial Cancer.
Proteomics Clin Appl
September 2025
AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan.
Background: Endometrial carcinoma (EC) represents a significant clinical challenge due to its pronounced molecular heterogeneity, directly influencing prognosis and therapeutic responses. Accurate classification of molecular subtypes (CNV-high, CNV-low, MSI-H, POLE) and precise tumor mutational burden (TMB) assessment is crucial for guiding personalized therapeutic interventions. Integrating proteomics data with advanced machine learning (ML) techniques offers a promising strategy for achieving precise, clinically actionable classification and biomarker discovery in EC.
View Article and Find Full Text PDFWidely Distributed Pigeon Paramyxovirus Sub-Genotypes Pose a Risk to Immunocompromised Humans.
Zoonoses Public Health
September 2025
Department of Production Animal Studies, Faculty of Veterinary Science, University of Pretoria, Onderstepoort, South Africa.
Introduction: Pigeon paramyxovirus type 1 (PPMV-1) is an antigenic variant of Avian Orthoavulavirus 1 (AOAV-1) (Newcastle disease virus) with a global distribution that causes lethal infections in pigeon and dove species. AOAV-1's infecting humans normally cause mild, self-limiting conjunctivitis, but since 2003, PPMV-1 has been associated with an increased number of severe and lethal respiratory and neurological infections in immunocompromised persons in the Netherlands, the USA, France, China and Australia.
Methods: PPMV-1's isolated from free-living pigeons and doves across South Africa from 2012 to 2024 were sequenced using conventional or next generation technologies.
Transcriptomic analysis of immune and metabolic responses in largemouth bass (Micropterus salmoides) challenged with Citrobacter freundii.
J Fish Biol
September 2025
College of Animal Science and Technology, Yangzhou University, Yangzhou, China.
Citrobacter freundii, a common zoonotic pathogen affecting humans, livestock and fish, is recognized for its substantial impact on largemouth bass (Micropterus salmoides) mortality. However, the mechanisms of C. freundii infection in largemouth bass remain poorly understood.
View Article and Find Full Text PDFFatty acid synthase in high and low lipid-producing strains of Mucor circinelloides: identification, phylogenetic analysis, and expression profiling during growth and lipid accumulation.
Biotechnol Lett
September 2025
Shandong Provincial Engineering Research Center for Precision Nutrition and Healthy Elderly Care, Qilu Medical University, 1678 Renmin West Road, Zibo, 255300, People's Republic of China.
Fatty acid synthase (FAS) is one of the most important enzymes in lipid biosynthesis, which can catalyze the reaction of acetyl-CoA and malonyl-CoA to produce fatty acids. However, the structure, function, and molecular mechanism of FAS regulating lipid synthesis in the fungus Mucor circinelloides are unclear. In the present study, two encoding fas genes in the high lipid-producing strain WJ11 and low lipid-producing strain CBS277.
View Article and Find Full Text PDF