Plants Diseases Prediction Framework: A Image-Based System Using Deep Learning

Kirola, Madhu; Joshi, Kapil; Chaudhary, Sumit; Singh, Neha; Anandaram, Harishchander; Gupta, Ashulekha

doi:10.1109/aic55036.2022.9848899

Cited by 23 publications

(5 citation statements)

References 10 publications

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“…It has been reported that smart food growing can be facilitated by the application of the Internet of Things (IoT), which is considered a technological advancement [51]. Furthermore, artificial intelligence techniques can be used for plant disease detection and prediction [52]. Both own research and literature data have shown the extraordinary usefulness of artificial intelligence and sensor technology in growth monitoring and assessment of fruit and vegetable quality.…”

Section: Discussionmentioning

confidence: 99%

Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars

Sabancı

2023

Sustainability

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In the present study, a deep learning-based two-scenario method is proposed to distinguish tomato seed cultivars. First, images of seeds of four different tomato cultivars (Sacher F1, Green Zebra, Pineapple, and Ozarowski) were taken. Each seed was then cropped on the raw image and saved as a new image. The number of images in the dataset was increased using data augmentation techniques. In the first scenario, these seed images were classified with four different CNN (convolutional neural network) models (ResNet18, ResNet50, GoogleNet, and MobileNetv2). The highest classification accuracy of 93.44% was obtained with the MobileNetv2 model. In the second scenario, 1280 deep features obtained from MobileNetv2 fed the inputs of the Bidirectional Long Short-Term Memory (BiLSTM) network. In the classification made using the BiLSTM network, 96.09% accuracy was obtained. The results show that different tomato seed cultivars can be distinguished quickly and accurately by the proposed deep learning-based method. The performed study is a great novelty in distinguishing seed cultivars and the developed innovative approach involving deep learning in tomato seed image analysis, and can be used as a comprehensive procedure for practical tomato seed classification.

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Section: Discussionmentioning

confidence: 99%

Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars

Sabancı

2023

Sustainability

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“…The problem of plant disease classification based on images plays a significant role in real-life scenarios, particularly in contributing to addressing issues related to the quality and quantity of agricultural produce. The results of research on leaf [10] plant village 19 75 0.9718 [11] plant village tomato leaf 10 14500 0.981 [12] plant village 30 53200 0.9843 [16] plantvillage 38 163,000 0.9014 [13] tomato leaf disease 7 735 0.96735 [17] Plantvillage 7 11,165 0.9571 [18] Plantvillage 14 3,000 0.98 [23] Plantvillage 14 54,305 0.9922 [24] Plantvillage 6 6,594 0.9635 [14] tomato leaf disease 8 10000 0.926 [15] plantvillage In the future, we will build new models or combine from many different models to further improve the accuracy of this problem.…”

Section: Discussionmentioning

confidence: 99%

“…This study [12], the identified diseases were categorized into three distinct groups: bacterial, viral, and fungal infections. The research delved into a thorough exploration of these aspects and employed a range of machine learning (ML) and deep learning (DL) techniques.…”

Section: Related Workmentioning

confidence: 99%

An Approach for Classification of Diseases on Leaves

2023

IJACSA

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In recent years, significant advancements have been made in the realm of plant disease classification, with a particular focus on leveraging the capabilities of deep learning techniques. This study delves into the utilization of renowned Convolutional Neural Network (CNN) models, including EfficientNetB5, Mo-bileNet, ResNet50, InceptionV3, and VGG16, for the purpose of plant disease classification. The core methodology involves employing transfer learning, wherein these established CNN models are employed as a foundation and subsequently finetuned using a publicly accessible plant disease dataset. The study also compared the results with some deep learning models and with state-of-the-art. Among the tested CNNs, EfficientNetB5 has shown the best performance. EfficientNetB5 has outperformed another model and obtained 99.2% classification accuracy.

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“…It is the statistical algorithm for classifying categorical class variables (Kirola et al, 2022). It analyses the relationship between a set of independent variables and dependent binary variables.…”

Section: Methodsmentioning

confidence: 99%

Deep learning-based method to identify disease-resistance proteins inOryza sativaand relative species

Dhiman,

Biswas,

Swain

et al. 2023

Preprint

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Rice (Oryza sativa) is a significant agricultural crop consumed by more than half of the global population. Its demand is expected to increase due to rising consumption and a growing global population. Moreover, the rice plant is frequently exposed to disease-causing pathogens, such as bacteria, fungi, viruses, and nematodes. Thus, cultivating disease-resistant varieties is an efficient way of disease control compared to pesticide applications. However, the rice plant has a well-defined defense system to prevent the onset of disease, including Pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) and effector-triggered immunity (ETI). The defense system is controlled by various disease-resistance proteins, such as resistance (R) proteins and pathogen recognition receptors (PRRs). Therefore, the identification of disease-resistance proteins not only reduces the amount of pesticides used in rice fields but also increases their yield. Though some resistant proteins have been characterized, their rapid identification, precise diagnosis, and appropriate management are still lacking. However, few methods based on sequence-similarity and de novo prediction, such as Machine Learning (ML), usually have low prediction power. In this study, we built a state-of-the-art classifier based on Deep Learning (DL) for the early detection of disease-resistance proteins in rice and related species. We compared the DL-based Multi-layer Perceptron (MLP) model with the five well-established ML-based methods using a protein dataset of rice and its related species. The DL-based MLP model outperformed all of the five classifiers on 10-fold cross-validation. The accuracy, Area Under Receiving Operating Characteristic (ROC) curve (AUC), F1-score, precision, and recall were superior in the DL-based MLP model. In conclusion, the MLP model is an effective DL model for predicting disease-resistance proteins with high scores in performance metrics. This study will provide insight to the breeders in developing disease-resistant rice varieties and assist in transforming traditional rice farming practices into a new age of smart rice farming.

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Plants Diseases Prediction Framework: A Image-Based System Using Deep Learning

Cited by 23 publications

References 10 publications

Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars

Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars

An Approach for Classification of Diseases on Leaves

Deep learning-based method to identify disease-resistance proteins inOryza sativaand relative species

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