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- A Lightweight Deep Learning-Based Model for Tomato Leaf Disease . . .
We propose the Deep Tomato Detection Network (DTomatoDNet), a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this The Convn kernels used in the proposed (DTomatoDNet) framework is 1 × 1, which reduces the number of parameters and helps in more detailed and descriptive
- Deep Learning for Precision Agriculture: Detecting Tomato Leaf Diseases . . .
Fig 1 Steps in Implementing Machine Learning Models for Plant Disease Detection Firstly, the research aims to establish a robust deep-learning model based on the VGG-16 architecture This model seeks to detect and categorize tomato leaf diseases effectively, offering farmers an accurate disease identification and intervention tool
- An efficient deep learning model for tomato disease detection
This has caused serious food safety issues and significantly reduced the economic benefits of tomato cultivation Consequently, rapid and accurate disease detection plays a crucial role in the prevention and control of tomato diseases [8, 9] Currently, the identification and control of tomato diseases primarily rely on empirical methods (Fig 3), which are characterized by low timeliness, poor
- Optimized Deep Learning Algorithms for Tomato Leaf Disease Detection . . .
In the direction of building a handheld device capable of tomato leaf disease detection, a Raspberry Pi 4 Model B was also used for evaluating and testing the models after training It consists of Broadcom BCM2711, Quad core Cortex-A72 (ARM v8) 1 5 GHz processor (San Jose, California), 2 GB RAM and a 16 GB SD card for storage running on
- Efficient deep learning-based tomato leaf disease detection through . . .
In the context of intelligent agriculture, tomato cultivation involves complex environments, where leaf occlusion and small disease areas significantly impede the performance of tomato leaf disease detection models To address these challenges, this study proposes an efficient Tomato Disease Detection Network (E-TomatoDet), which enhances tomato leaf disease detection effectiveness by
- Tomato Leaf Disease Detection Using Deep Learning Based Model
Tomato yield has been significantly affected by four distinct types of leaf diseases: Alternaria leaf spot, Brown spot, Mosaic, and Grey spot However, a reliable and rapid disease detector for Tomatoes is still lacking in the current body of research, which could threaten the industry's growth This research suggests a deep learning strategy for immediate-form Tomato leaf disease detection
- Deep learning-based classification, detection, and segmentation of . . .
The slow detection periods of conventional approaches are insufficient for the timely detection of tomato diseases Deep learning has emerged as a promising avenue for early disease identification According to Albahli and Nawaz (2022), a tomato leaf disease detection model called DenseNet-77 with CornerNet achieved the highest accuracy of
- Deep Learning for Early Detection of Tomato Leaf Diseases: A ResNet-18 . . .
process for identifying tomato leaf diseases uses six classifiers In order to confirm the suggested pipeline's ability to compete, the experimental results are also compared with earlier studies on the classification of tomato leaf diseases The circumstances of a tomato plant have been determined
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