Horticulturae

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15 pages, 2386 KiB

Open AccessEditor’s ChoiceArticle

Tomato Leaf Disease Recognition via Optimizing Deep Learning Methods Considering Global Pixel Value Distribution

by Zheng Li, Weijie Tao, Jianlei Liu, Fenghua Zhu, Guangyue Du and Guanggang Ji

Horticulturae 2023, 9(9), 1034; https://doi.org/10.3390/horticulturae9091034 - 14 Sep 2023

Cited by 2 | Viewed by 1564

In image classification of tomato leaf diseases based on deep learning, models often focus on features such as edges, stems, backgrounds, and shadows of the experimental samples, while ignoring the features of the disease area, resulting in weak generalization ability. In this study, [...] Read more.

In image classification of tomato leaf diseases based on deep learning, models often focus on features such as edges, stems, backgrounds, and shadows of the experimental samples, while ignoring the features of the disease area, resulting in weak generalization ability. In this study, a self-attention mechanism called GD-Attention is proposed, which considers global pixel value distribution information and guide the deep learning model to give more concern on the leaf disease area. Based on data augmentation, the proposed method inputs both the image and its pixel value distribution information to the model. The GD-Attention mechanism guides the model to extract features related to pixel value distribution information, thereby increasing attention towards the disease area. The model is trained and tested on the Plant Village (PV) dataset, and by analyzing the generated attention heatmaps, it is observed that the disease area obtains greater weight. The results achieve an accuracy of 99.97% and 27 MB parameters only. Compared to classical and state-of-the-art models, our model showcases competitive performance. As a next step, we are committed to further research and application, aiming to address real-world, complex scenarios. Full article

(This article belongs to the Special Issue Smart Horticulture, Plant Secondary Compounds and Their Applications)

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19 pages, 2613 KiB

Open AccessEditor’s ChoiceArticle

Tomato Leaf Disease Classification via Compact Convolutional Neural Networks with Transfer Learning and Feature Selection

by Omneya Attallah

Horticulturae 2023, 9(2), 149; https://doi.org/10.3390/horticulturae9020149 - 22 Jan 2023

Cited by 31 | Viewed by 11294

Abstract

Tomatoes are one of the world’s greatest valuable vegetables and are regarded as the economic pillar of numerous countries. Nevertheless, these harvests remain susceptible to a variety of illnesses which can reduce and destroy the generation of healthy crops, making early and precise [...] Read more.

Tomatoes are one of the world’s greatest valuable vegetables and are regarded as the economic pillar of numerous countries. Nevertheless, these harvests remain susceptible to a variety of illnesses which can reduce and destroy the generation of healthy crops, making early and precise identification of these diseases critical. Therefore, in recent years, numerous studies have utilized deep learning (DL) models for automatic tomato leaf illness identification. However, many of these methods are based on a single DL architecture that needs a high computational ability to update these hyperparameters leading to a rise in the classification complexity. In addition, they extracted large dimensions from these networks which added to the classification complication. Therefore, this study proposes a pipeline for the automatic identification of tomato leaf diseases utilizing three compact convolutional neural networks (CNNs). It employs transfer learning to retrieve deep features out of the final fully connected layer of the CNNs for more condensed and high-level representation. Next, it merges features from the three CNNs to benefit from every CNN structure. Subsequently, it applies a hybrid feature selection approach to select and generate a comprehensive feature set of lower dimensions. Six classifiers are utilized in the tomato leaf illnesses identification procedure. The results indicate that the K-nearest neighbor and support vector machine have attained the highest accuracy of 99.92% and 99.90% using 22 and 24 features only. The experimental results of the proposed pipeline are also compared with previous research studies for tomato leaf diseases classification which verified its competing capacity. Full article

(This article belongs to the Special Issue Smart Horticulture, Plant Secondary Compounds and Their Applications)

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11 pages, 2701 KiB

Open AccessArticle

Wild Chrysanthemums Core Collection: Studies on Leaf Identification

by Toan Khac Nguyen, L. Minh Dang, Hyoung-Kyu Song, Hyeonjoon Moon, Sung Jae Lee and Jin Hee Lim

Horticulturae 2022, 8(9), 839; https://doi.org/10.3390/horticulturae8090839 - 13 Sep 2022

Cited by 4 | Viewed by 2776

Abstract

Wild chrysanthemums mainly present germplasm collections such as leaf multiform, flower color, aroma, and secondary compounds. Wild chrysanthemum leaf identification is critical for farm owners, breeders, and researchers with or without the flowering period. However, few chrysanthemum identification studies are related to flower [...] Read more.

Wild chrysanthemums mainly present germplasm collections such as leaf multiform, flower color, aroma, and secondary compounds. Wild chrysanthemum leaf identification is critical for farm owners, breeders, and researchers with or without the flowering period. However, few chrysanthemum identification studies are related to flower color recognition. This study contributes to the leaf classification method by rapidly recognizing the varieties of wild chrysanthemums through a support vector machine (SVM). The principal contributions of this article are: (1) an assembled collection method and verified chrysanthemum leaf dataset that has been achieved and improved; (2) an adjusted SVM model that is offered to deal with the complex backgrounds presented by smartphone pictures by using color and shape classification results to be more attractive than the original process. As our study presents, the proposed method has a viable application in real-picture smartphones and can help to further investigate chrysanthemum identification. Full article

(This article belongs to the Special Issue Smart Horticulture, Plant Secondary Compounds and Their Applications)

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