ArticleSensors (Basel, Switzerland)2021
AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 146 citations in OpenAlex.
- Deep Learning for Disease Detection: Building a Leaf Image Classifier for Roses.Sensors (Basel, Switzerland) · 2026Article
- Automated Guava Disease Detection Using Transfer Learning With ResNet-101.Food science & nutrition · 2025Article
- SCA-MobiPlant: smartphone-deployed multistage attention fusion model for accurate field detection of chili leaf curl complex.Plant methods · 2025Article
- Vase-Life Monitoring System for Cut Flowers Using Deep Learning and Multiple Cameras.Plants (Basel, Switzerland) · 2025Article
- Smart crop disease monitoring system in IoT using optimization enabled deep residual network.Scientific reports · 2025Article
- Developing sustainable system based on transformers algorithms to predict the Dubas insects diseases in palm leaves.Frontiers in plant science · 2025Article
- Correction: Almadhor et al. AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery.Sensors (Basel, Switzerland) · 2024Article
- Plant disease recognition using residual convolutional enlightened Swin transformer networks.Scientific reports · 2024Article
- Effective feature selection based HOBS pruned- ELM model for tomato plant leaf disease classification.PloS one · 2024Article
- Improvement of the YOLOv5 Model in the Optimization of the Brown Spot Disease Recognition Algorithm of Kidney Bean.Plants (Basel, Switzerland) · 2023Article
- The Novel Combination of Nano Vector Network Analyzer and Machine Learning for Fruit Identification and Ripeness Grading.Sensors (Basel, Switzerland) · 2023Article
- Based on the multi-scale information sharing network of fine-grained attention for agricultural pest detection.PloS one · 2023Article
- Palm tree disease detection and classification using residual network and transfer learning of inception ResNet.PloS one · 2023Article
- Brinjal leaf diseases detection based on discrete Shearlet transform and Deep Convolutional Neural Network.PloS one · 2023Article
- Article
- Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model.Sensors (Basel, Switzerland) · 2022Article
- Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction.Sensors (Basel, Switzerland) · 2022Article
- A Performance Comparison of Classification Algorithms for Rose Plants.Computational intelligence and neuroscience · 2022Article
- Improved AlexNet with Inception-V4 for Plant Disease Diagnosis.Computational intelligence and neuroscience · 2022Article
- Maize leaf disease identification based on WG-MARNet.PloS one · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 5 institutions in 4 countries.
Funding
Abstract
Plant diseases can cause a considerable reduction in the quality and number of agricultural products. Guava, well known to be the tropics' apple, is one significant fruit cultivated in tropical regions. It is attacked by 177 pathogens, including 167 fungal and others such as bacterial, algal, and nematodes. In addition, postharvest diseases may cause crucial production loss. Due to minor variations in various guava disease symptoms, an expert opinion is required for disease analysis. Improper diagnosis may cause economic losses to farmers' improper use of pesticides. Automatic detection of diseases in plants once they emerge on the plants' leaves and fruit is required to maintain high crop fields. In this paper, an artificial intelligence (AI) driven framework is presented to detect and classify the most common guava plant diseases. The proposed framework employs the ΔE color difference image segmentation to segregate the areas infected by the disease. Furthermore, color (RGB, HSV) histogram and textural (LBP) features are applied to extract rich, informative feature vectors. The combination of color and textural features are used to identify and attain similar outcomes compared to individual channels, while disease recognition is performed by employing advanced machine-learning classifiers (Fine KNN, Complex Tree, Boosted Tree, Bagged Tree, Cubic SVM). The proposed framework is evaluated on a high-resolution (18 MP) image dataset of guava leaves and fruit. The best recognition results were obtained by Bagged Tree classifier on a set of RGB, HSV, and LBP features (99% accuracy in recognizing four guava fruit diseases (Canker, Mummification, Dot, and Rust) against healthy fruit). The proposed framework may help the farmers to avoid possible production loss by taking early precautions.
Indexed as
Identifiers
What OpenQuestion holds
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.