Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI

Ghosh, Promila; Mondal, Amit Kumar; Chatterjee, Siddhartha; Masud, Mehedi; Meshref, Hossam; Bairagi, Anupam Kumar

doi:10.3390/math11102241

Cited by 20 publications

(9 citation statements)

References 47 publications

(87 reference statements)

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“…The proposed model is trained with a dataset of different diseases affecting sunflowers. The results of the proposed research study achieved an F1 score of 83.45 and an overall classification accuracy of 83.59%.Gosh et al [4] developed a hybrid model with transfer learning and a simple CNN to detect sunflower diseases. Of the eight models tested on a dataset consisting of four different classes, the VGG19 + CNN hybrid model achieved the best results in terms of sensitivity, recall, f1 score, and accuracy.…”

Section: Literature Reviewmentioning

confidence: 90%

“…The agricultural economy suffers greatly from productivity losses. Preventing productivity loss is possible by diagnosing diseases in time and taking precautions [4]. Thanks to early detection, we can save plants and prevent losses.…”

Section: Introductionmentioning

confidence: 99%

“…Sunflowers are bacteria, fungi, nematodes, parasites, viruses, etc. are exposed to many diseases [4]. The dataset includes three disease classes and one healthy sunflower leaf class.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Approaches for Sunflower Disease Classification: A Study of Convolutional Neural Networks with Squeeze and Excitation Attention Blocks

Ünal,

Dudak

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Diseases in agricultural plants are one of the most important problems of agricultural production. These diseases cause decreases in production and this poses a serious problem for food safety. One of the agricultural products is sunflower. Helianthus annuus, generally known as sunflower, is an agricultural plant with high economic value grown due to its drought-resistant and oil seeds. In this study, it is aimed to classify the diseases seen in sunflower leaves and flowers by applying deep learning models. First of all, it was classified with ResNet101 and ResNext101, which are pre-trained CNN models, and then it was classified by adding squeeze and excitation blocks to these networks and the results were compared. In the study, a data set containing gray mold, downy mildew, and leaf scars diseases affecting the sunflower crop was used. In our study, original Resnet101, SE-Resnet101, ResNext101, and SE-ResNext101 deep-learning models were used to classify sunflower diseases. For the original images, the classification accuracy of 91.48% with Resnet101, 92.55% with SE-Resnet101, 92.55% with ResNext101, and 94.68% with SE-ResNext101 was achieved. The same models were also suitable for augmented images and classification accuracies of Resnet101 99.20%, SE-Resnet101 99.47%, ResNext101 98.94%, and SE-ResNext101 99.84% were achieved. The study revealed a comparative analysis of deep learning models for the classification of some diseases in the Sunflower plant. In the analysis, it was seen that SE blocks increased the classification performance for this dataset. Application of these models to real-world agricultural scenarios holds promise for early disease detection and response and may help reduce potential crop losses.

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Section: Literature Reviewmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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Deep Learning Approaches for Sunflower Disease Classification: A Study of Convolutional Neural Networks with Squeeze and Excitation Attention Blocks

Ünal,

Dudak

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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“…Malik et al [38] proposed a hybrid transfer learning (TL) model based on VGG-16 and MobileNet that can recognize five types of sunflower diseases, but the method is about 10% lower in accuracy than the proposed method. Ghosh et al [39] developed a hybrid model with TL and simple CNN to recognize sunflower diseases and obtained 93.00% accuracy. Gulzar et al [40] used several classical deep learning models trained in multiple iterations on a sunflower disease dataset and achieved a high accuracy of 97.60%.…”

Section: Comparison With Existing Deep Learning Methodsmentioning

confidence: 99%

TeenyNet: a novel lightweight attention model for sunflower disease detection

Zhong,

Tong

2023

Meas. Sci. Technol.

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Sunflower is one of the oilseed crops which is popularly and widely cultivated globally and contributes significantly to human health. Leaf diseases of sunflower seriously affect the growth and yield of sunflower, which directly leads to the loss of agricultural economy. However, existing machine learning algorithms and deep learning techniques are mainly based on large models with attention mechanisms, which lack considerations in computational performance, especially model size. Therefore, this study proposes a lightweight model called TeenyNet to break through the dilemma. First, the designed global multi-frequency feature extraction module decomposes the image to extract multi-frequency multi-scale features. Then, a parameter-free maximum pooling layer further extracts edge and texture features and simplifies the network complexity through downsampling, after which the proposed lightweight dual fusion attention and multi-branching structure fuses all the feature vectors to enhance multidimensional feature learning and accelerate the model convergence. Finally, the fully connected linear layer solves the multi-classification problem of sunflower disease under natural illumination background conditions. The experimental results show that TeenyNet obtains the highest accuracy of 98.94% for sunflower disease recognition with a minimum size of 143 KB and has better recognition performance in comparison experiments. TeenyNet can be effectively used for the detection of sunflower leaf diseases to achieve disease prevention and control.

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“…Recently, a few of scientific literature has emerged, focusing on utilizing ML algorithms to predict the performance of sunflower crops. Predominantly, the application of machine learning models was focused on the prediction of yield outcomes 10 , 11 , as well as the prediction of traits encompassing disease identification and resistance 12 , 13 , seed quality assessments 14 , 15 , thereby facilitating the selection of superior sunflower hybrids. Furthermore, machine learning models were also used to analyze images and remote sensing of sunflower plants for various traits 16 – 18 .…”

Section: Introductionmentioning

confidence: 99%

Oil yield prediction for sunflower hybrid selection using different machine learning algorithms

Cvejić,

Hrnjaković,

Jocković

et al. 2023

Sci Rep

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Due to the increased demand for sunflower production, its breeding assignment is the intensification of the development of highly productive oil seed hybrids to satisfy the edible oil industry. Sunflower Oil Yield Prediction (SOYP) can help breeders to identify desirable new hybrids with high oil yield and their characteristics using machine learning (ML) algorithms. In this study, we developed ML models to predict oil yield using two sets of features. Moreover, we evaluated the most relevant features for accurate SOYP. ML algorithms that were used and compared were Artificial Neural Network (ANN), Support Vector Regression, K-Nearest Neighbour, and Random Forest Regressor (RFR). The dataset consisted of samples for 1250 hybrids of which 70% were randomly selected and were used to train the model and 30% were used to test the model and assess its performance. Employing MAE, MSE, RMSE and R2 evaluation metrics, RFR consistently outperformed in all datasets, achieving a peak of 0.92 for R2 in 2019. In contrast, ANN recorded the lowest MAE, reaching 65 in 2018 The paper revealed that in addition to seed yield, the following characteristics of hybrids were important for SOYP: resistance to broomrape (Or) and downy mildew (Pl) and maturity. It was also disclosed that the locality feature could be used for the estimation of sunflower oil yield but it is highly dependable on weather conditions that affect the oil content and seed yield. Up to our knowledge, this is the first study in which ML was used for sunflower oil yield prediction. The obtained results indicate that ML has great potential for application in oil yield prediction, but also selection of parental lines for hybrid production, RFR algorithm was found to be the most effective and along with locality feature is going to be further evaluated as an alternative method for genotypic selection.

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Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI

Cited by 20 publications

References 47 publications

Deep Learning Approaches for Sunflower Disease Classification: A Study of Convolutional Neural Networks with Squeeze and Excitation Attention Blocks

Deep Learning Approaches for Sunflower Disease Classification: A Study of Convolutional Neural Networks with Squeeze and Excitation Attention Blocks

TeenyNet: a novel lightweight attention model for sunflower disease detection

Oil yield prediction for sunflower hybrid selection using different machine learning algorithms

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