Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

Abstract

Humans depend heavily on agriculture, which is the main source of prosperity. The various plant diseases that farmers must contend with have constituted a lot of challenges in crop production. The main issues that should be taken into account for maximizing productivity are the recognition and prevention of plant diseases. Early diagnosis of plant disease is essential for maximizing the level of agricultural yield as well as saving costs and reducing crop loss. In addition, the computerization of the whole process makes it simple for implementation. In this paper, an intelligent method based on deep learning is presented to recognize nine common tomato diseases. To this end, a residual neural network algorithm is presented to recognize tomato diseases. This research is carried out on four levels of diversity including depth size, discriminative learning rates, training and validation data split ratios, and batch sizes. For the experimental analysis, five network depths are used to measure the accuracy of the network. Based on the experimental results, the proposed method achieved the highest F1 score of 99.5%, which outperformed most previous competing methods in tomato leaf disease recognition. Further testing of our method on the Flavia leaf image dataset resulted in a 99.23% F1 score. However, the method had a drawback that some of the false predictions were of tomato early light and tomato late blight, which are two classes of fine-grained distinction.

Revolutionizing Plant Disease Detection in Leaves: An Innovative Hybrid ABOCNN Framework for Advanced and Accurate Identification

“…The suggested approach has been tested with leaf samples and run in the MATLAB program on the Windows 10 operating system. [26] employed a 3D CNN model for the classification of charcoal rot illness because to its excellent classification accuracy and capacity for automatically acquiring the spatio-temporal characteristics without handcrafting [27]. The findings showed that the model worked well on both training and test information.…”

Tomato Leaf Classification Using Computer Vision and Deep Learning: Comparing Different EfficientNets

“…Considerable advancements have been achieved in detecting diseases in various plants, including bananas, cucumbers, apples, tomatoes, rice, and peppers (Kanda et al, 2022;Zhou et al, 2019). Mohanty et al (2016) analyzed 54.306 plant leaf images from a range of 38 class labels.…”

“…Source:Kanda et al (2022),Panno et al (2021),Das (2020),Gilardi et al (2021),Rivarez et al (2021),Rodrigues andFurlong (2022) …”

Text-to-Image Generation using Generative Adversarial Network

“…CNN also extracts extra features from images, such as colors, borders, and textures. According to reports, the proposed model's predictions were 98.49%.The study developed [17] a residual neural network algorithm-based intelligent method for identifying nine prevalent tomato illnesses. A standard convolutional neural network architecture's fundamental building blocks, known as layers, are included in the technique of the suggested network approach, which is discussed in the study.…”