Sign in to use this feature.

Years

Between: -

Article Types

Countries / Regions

Search Results (2)

Search Parameters:
Journal = IJPB
Section = Application of Artificial Intelligence in Plant Biology

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
8 pages, 1259 KiB  
Technical Note
LeafArea Package: A Tool for Estimating Leaf Area in Andean Fruit Species
by Pedro Alexander Velasquez-Vasconez and Danita Andrade Díaz
Int. J. Plant Biol. 2024, 15(1), 102-109; https://doi.org/10.3390/ijpb15010009 - 29 Jan 2024
Cited by 1 | Viewed by 1453
Abstract
The LeafArea package is an innovative tool for estimating leaf area in six Andean fruit species, utilizing leaf length and width along with species type for accurate predictions. This research highlights the package’s integration of advanced machine learning algorithms, including GLM, GLMM, Random [...] Read more.
The LeafArea package is an innovative tool for estimating leaf area in six Andean fruit species, utilizing leaf length and width along with species type for accurate predictions. This research highlights the package’s integration of advanced machine learning algorithms, including GLM, GLMM, Random Forest, and XGBoost, which excels in predictive accuracy. XGBoost’s superior performance is evident in its low prediction errors and high R2 value, showcasing the effectiveness of machine learning in leaf area estimation. The LeafArea package, thus, offers significant contributions to the study of plant growth dynamics, providing researchers with a robust and precise tool for informed decision making in resource allocation and crop management. Full article
(This article belongs to the Section Application of Artificial Intelligence in Plant Biology)
Show Figures

Figure 1

18 pages, 3811 KiB  
Review
Machine Learning and Image Processing Techniques for Rice Disease Detection: A Critical Analysis
by Md. Mehedi Hasan, A F M Shahab Uddin, Mostafijur Rahman Akhond, Md. Jashim Uddin, Md. Alamgir Hossain and Md. Alam Hossain
Int. J. Plant Biol. 2023, 14(4), 1190-1207; https://doi.org/10.3390/ijpb14040087 - 18 Dec 2023
Cited by 12 | Viewed by 7788
Abstract
Early rice disease detection is vital in preventing damage to agricultural product output and quantity in the agricultural field. Manual observations of rice diseases are tedious, costly, and time-consuming, especially when classifying disease patterns and color while dealing with non-native diseases. Hence, image [...] Read more.
Early rice disease detection is vital in preventing damage to agricultural product output and quantity in the agricultural field. Manual observations of rice diseases are tedious, costly, and time-consuming, especially when classifying disease patterns and color while dealing with non-native diseases. Hence, image processing and Machine Learning (ML) techniques are used to detect rice disease early and within a relatively brief time period. This article aims to demonstrate the performance of different ML algorithms in rice disease detection through image processing. We critically examined different techniques, and the outcomes of previous research have been reviewed to compare the performance of rice disease classifications. Performance has been evaluated based on the criteria of feature extraction, clustering, segmentation, noise reduction, and level of accuracy of disease detection techniques. This paper also showcases various algorithms for different datasets in terms of training methods, image preprocessing with clustering and filtering criteria, and testing with reliable outcomes. Through this review, we provide valuable insights into the current state of ML-based approaches for the early detection of rice diseases, and assist future research and improvement. In addition, we discuss several challenges that must be overcome in order to achieve effective identification of rice diseases. Full article
(This article belongs to the Section Application of Artificial Intelligence in Plant Biology)
Show Figures

Figure 1

Back to TopTop