Machine Learning in Cereal Crops Disease Detection

Machine Learning in Cereal Crops Disease Detection: Comparison

Please note this is a comparison between Version 4 by Peter Tang and Version 3 by Peter Tang.

Cereals are an important and major source of the human diet. They constitute more than two-thirds of the world’s food source and cover more than 56% of the world’s cultivatable land. These important sources of food are affected by a variety of damaging diseases, causing significant loss in annual production. In this regard, detection of diseases at an early stage and quantification of the severity has acquired the urgent attention of researchers worldwide. One emerging and popular approach for this task is the utilization of machine learning techniques.

cereal crop
plant disease
machine learning
deep learning

1. Introduction

Advancements in the area of machine learning and computer vision in the past decade had had a profound effect on the utilization of machine learning techniques in different sectors ^[1]. Machine learning approaches are being used from the medical ^{[2][3][4][5][6][7][8][9]} to the security sector ^[10]. Recently, many works ^[11] have been undertaken on the application of machine learning in the agriculture sector for the detection of plant diseases, such as coffee ^[12] and Enset ^[13], Crop yield prediction ^[14], quality and growth monitoring ^[15][16], supply chain performance ^[17], and water stress determination ^[18].

Plants constitute 98% of the world’s diet, two-thirds of which are Cereals ^[19]. The eight major kinds of cereal, wheat, maize, rice, barley, sorghum, oats, millets, and rye cover 56 percent of the world’s arable land. Wheat, maize, and rice account for 80% of global cereal production ^[19]. Plant diseases are the major cause of global crop yield reduction, resulting in 10% loss of all the global food production ^[20]. The major plant disease-causing pathogens are viruses, bacteria, Oomycetes, fungi, nematodes, and other parasitic plants ^[20]. When infections occur to a large extent, losses to cereal crop production could reach as high as 50% ^[21]. Many laboratory techniques are available for the identification and detection of plant pathogens ^[20], but rapid and early detection is an important factor in the successful containment and control ^[22].

2. Cereal Crops and Diseases

Cereal is a crop closely related to grass and that is cultivated for its seed and is consumed as food by humans ^[23]. According to the Cereal Disease, Methodology Manual ^[19], the eight major kinds of cereal, covering 56% percent of the world’s arable land are Wheat, Maize, Rice, Barley, Sorghum, Oats, Millet, and Rye.

2.1. Wheat

Wheat is the most dominant and important source of food for humans and livestock ^[24]. It is the main ingredient in flour, which is used in the making of bread, biscuits, and pastry ^[19]. Wheat is cultivated across all parts of the earth, from Russia in the northern hemisphere to Argentina in the south ^[24]. Diseases pose a serious threat to the global production of wheat ^[20]. Diseases on wheat are caused by a variety of pathogens. These are, Fungai, Viruses, Bacteria, Insects and Nematodes ^[25]. Some of the commonly occurring wheat diseases are given in Table 1.

Table 1. Some wheat disease types and causing pathogens ^[25].

Pathogen	Disease
	Leaf Rust (Brown Rust), Stem Rust (Black Rust),
	Stripe Rust (Yellow Rust), Common Root Rot,
Fungus	Common and Dwarf Bunt (Stinking Smut),

Root Lesion Nematode

2.2. Maize (Corn)

Maize is an important staple food crop that is grown all over the globe. It is the largest grown cereal per unit area, yielding 785 million tons annually ^[26]. Besides being a source of food, maize, and its products are used as raw materials for many industrial applications. Maize is prone to many types of diseases caused by a variety of pathogens. Fungal pathogens are the major causes of maize disease, while bacterial and viral diseases are less common but pose a serious threat ^[21][27]. Commonly occurring maize diseases are given in Table 2.

Table 2. Some Maize disease types and causing pathogens ^[25][27].

Pathogen	Disease

Table 3. Some Rice disease types and causing pathogens ^[20][31].

Pathogen	Disease
Fungus
	Fungus	Gray leaf spot, Brown spot,


	Wheat Blast,

Virus	Barley stripe mosaic
Virus	Cereal tillering virus

2.5. Sorghum

Sorghum is the fifth most important cereal crop after wheat, maize, rice, and barley ^[35]. It is cultivated around the globe and used as a 0 source of food and energy, when used as a bio-fuel ^[36]. Sorghum production is highly affected by fungal and viral diseases, at times causing around 28% loss in production ^[37]. Some commonly occurring sorghum diseases are presented in Table 5, Table 6 and Table 7.

Table 5. Some Sorghum disease types and causing pathogens ^[20][37].

Pathogen	Disease

^[20].

Pathogen	Disease

Some Rye disease types and causing pathogens ^[20].

Pathogen	Disease
Fungus

UAV system and photo-bike used for hyperspectral imaging of wheat farms ^[43].

Summary of various wheat leaf disease datasets is presented in Table 8.

Table 8. Performance comparison of selected studies on machine learning based wheat disease detection and corresponding datasets.

Citatation	Year	Data Type	# of Classes	Sample Size	Method	Accuracy %
Bao et al. ^[38]	Anthracnose, Leaf blight, Zonate leaf spot
	2021	Image	3	360	SVM	93.3%	Tar spot, Charcoal rot
Sood et al. ^[45]	2020	Image	3	876	VGG16	99.07%	Rust, Gray leaf spot
Bacteria	Bacterial stripe
Fungus	Crown rust, Stem rust, Powdery mildew
	Smut disease, Leaf blight


Mukhtar et al. ^[46]
^]

	2019	Hyper-spectral	2	145	Linear Regression	0.75R²
Huang et al. ^[41]	2019	Hyper-spectral	2	89	SVM	85.7%

3.2. Machine Learning in Rice Disease Detection

Identification and classification of 12 types of rice leaf diseases using MobileNetV2 architecture and attention mechanism were proposed by Chen et al. ^[52]. The MobileNetV2 architecture was pre-trained on the ImageNet dataset and fine-tuned by using the transfer learning approach on a smaller local dataset. The authors utilized Channel Attention Mechanism (CAM) to better learn the inter-channel relationships. For fine-tuning and testing their proposed model, the authors collected a total of 1100 images of healthy and disease rice leaves. These 660 were compiled from various sources on the internet and 440 were collected from the field. The proposed model achieved an average classification accuracy of 99.67%. Similarly, Wang et al. ^[53] proposed a MobileNetv2 based approach for the classification of three types of rice leaf diseases by utilizing attention mechanism and Bayesian optimization. Model training and validation were performed on a public dataset of 2370 images belonging to three classes of rice disease and one healthy class. The authors achieved a classification accuracy of 94.65%.

Liang et al. ^[54] proposed a convolutional neural network-based rice blast disease detection approach. The authors proposed two CNN architectures, the first network containing four convolutional layers, four max-pooling layers, and three fully connected layers, and ReLU after each layer (Figure 3a) and a second network having the same convolutional layers and max-pooling layer structure as the first network, but with two additional fully connected layers as shown in (Figure 3b). The two models were trained on a custom dataset of 5808 images of healthy and rice blast infected leaves. The dataset was collected on-site and is divided into 2906 positive (rice blast infected) and 2902 healthy images. The authors utilized 5-fold cross-validation and a selected the second model due to its inherent stability on small datasets and chieved an accuracy of 95.83%. The proposed approach was compared to hand-crafted approaches like Local Binary Patterns Histogram (LBPH), Haar-WT. The comparison result suggests that the proposed CNN method achieves superior feature extraction and classification results. A similar approach for the detection and classification of three classes of rice disease was proposed by Rahman et al. ^[55]. The authors proposed a convolutional neural network trained on a dataset of 300 images containing three types of rice leaf disease (Brown spot, Leaf blight, and Hispa) and one healthy class. The model achieved a classification accuracy of 90%. This low classification accuracy is a result of the small dataset size the authors used and the lack of utilizing transfer learning. Ramesh et al. ^[56] proposed a convolutional neural network approach for the detection of three classes of rice disease. The authors utilized HSV color space for the separation of background and foreground and the K-means algorithm for disease segmentation.

Figure 3. Deep Convolutional Neural Network architecture for the detection of rice blast ^[54].

A random forest classifier for the detection and classification of three types of rice leaf disease was proposed by Saha and Ahsan ^[57]. A local dataset compromising a total of 276 images of healthy and infected rice leaves was collected by the authors for testing and training their proposed algorithm. Feature extraction was implemented by using intensity moments. The proposed approach achieved a classification accuracy of 91.47%. A deep learning method for the detection of 15 different rice diseases was implemented by Chen et al. ^[58]. The authors developed a deep learning architecture based on the fusion of existing DenseNet and Inception architectures. For testing the proposed model, the authors compiled a dataset consisting of 500 images belonging to 15 classes of rice disease. Their proposed model achieved a classification accuracy of 94.07%.

Summary of various rice leaf disease datasets is presented in Table 9.

Table 9. Performance comparison of selected studies on machine learning based rice disease detection and corresponding datasets.

Citatation	Year	Data Type	# of Classes	Sample Size	Method	Accuracy %
Chen et al. ^[52]	2021	Image	12	1100	MobileNetV2	99.67%

7448


faster R-CNN

	96.21%

3.3. Machine Learning in Maize Disease Detection

An Enhanced CNN for the detection of nine classes of maize leaf disease was proposed by Agarwal et al. ^[63]. They proposed a convolutional neural network with receptive field enlargement to enhance the feature extraction performance of the CNN, which is required due to the complexity of maize leaf images. To accomplish this task, the authors collected a dataset of 500 images of maize leaves belonging to nine different classes of maize leaf disease at different stages. The performance of the proposed approach was compared to existing models like AlexNet and GoogleNet and provided an improved classification accuracy of 95.12%. Sibiya et al. ^[64] developed a convolutional neural network for the detection of three different maize leaf diseases by using the Neuroph framework for the java programming language. The proposed approach gave a classification accuracy of 93.5%.

Barman et al. ^[65] proposed a MobileNet architecture-based maize leaf disease detection that will be deployed on Android mobile devices. The authors utilized a transfer learning approach to fine-tune the pre-trained MobileNet architecture. For this task, they used a public dataset (PlantVillage) with a total of 3852 images of four different classes of maize leaf diseases. The proposed approach yielded an accuracy of 94.53%.

Hasan et al. ^[66] proposed a hybrid network by combining a convolutional neural network and bi-directional LSTM for the detection of nine classes of maize leaf diseases. bi-LSTM was selected by the authors to better accelerate CNN’s classification accuracy and increase the co-relation among extracted features. Training of the model was performed on the PlantVillage dataset, which contains 2500 images of maize leaves affected by nine different types of diseases. They implemented various image augmentation techniques and increased the size of the dataset to 29,065 images. The proposed approach achieved a classification accuracy of 99.02%, exceeding existing deep learning methods.

Xu et al. ^[67] proposed a multi-scale convolutional global pooling convolutional neural network based on the AlexNet and Inception architecture. The proposed model improves on the AlexNet architecture by replacing the last fully connected layer with a global pooling layer and adding a batch normalization layer. This is implemented to solve the low accuracy achieved and the large training data size required when utilizing transfer learning. Training and testing of the proposed model were performed on the PlantVillage dataset. The authors found that the proposed approach improves average precision by more than 2% when compared to AlexNet. A VGG16 deep learning architecture-based maize disease identification was proposed by Tian ^[68]. In this work, a transfer learning approach was used to fine-tune the pre-trained VGG16 architecture on a dataset consisting of 7858 images of maize leaves affected by six types of diseases. The proposed method achieved a classification accuracy of 96.8%. Summary of various maize leaf disease datasets is presented in Table 10.

Table 10. Performance comparison of selected studies on machine learning based maize disease detection and corresponding datasets.

Citatation	Year	Data Type	# of Classes	Sample Size	Method	Accuracy %
Agarwal et al. ^[52]	2021	Image	9	500	CNN	95.12%
Wang et al. ^[53]
Sibiya et al. ^[64]	2021		2019 Image	Image (PlantVillage) 3	9 2370	2500 MobileNetV2	94.65%
		CNN			95.5%		Liang et al. ^[54]
Barman et al. ^[65]	2021		2019 Image	2021 Image 11	Image (PlantVillage) 1 440	9 5808	2500 MobileNet	92%
Kumar et.al ^[47]	2021	Virus	Streak disease

Table 6. Some Oats disease types and causing pathogens


Root rot, Crown rot, Snow mold
Bacteria
	Tan Spot
	Bacterial Stripe (Black Chaff),
Bacteria	Basal Glume Rot and Bacterial Leaf Blight,
	Bacterial Spike Blight (Gummosis)

2.4. Barley

Barley is an important staple food cereal crop, although it is produced in much less quantity than wheat, maize, and rice ^[19]. It is farmed in significant quantities in sub-Saharan countries like Ethiopia ^[32], where barley adaptation to high altitude environments makes it an important source of food and beverages for millions of people ^[33]. Barley is affected by over 80 different diseases caused by a variety of pathogens ^[34]. Some of these are summarized in Table 4.

Table 4. Some Barley disease types and causing pathogens ^[19][20][34].

Pathogen	Disease
	Leaf brown spot, Rice blast, Sheath rot
Fungus	Stripe rust, Leaf rust, Stem rust
	Stripe Rust (Yellow Rust)
	Common rust, Northern leaf blight

Powdery mildew, Downy mildew	Common rust, Smut,
	CNN			MobileNetV2		95.83%
		93.5%	Halo blight
Virus		Virus	Yellow dwarf
Fungus	Snow mold, Brown rust, Ergot
	Eye spot, Sharp eyespot
	Southern leaf blight, Smut
Northernl eaf blight, Southern leaf blight
Bacteria
	Net blotch, Spot blotch, Stripe disease	Bacteria
	Bacteria	Rahman et.al ^[55	Bacterial blight
	^]
Hasan et al. ^[66	Bacterial blightYellow dwarf	2021	^]	Image	2020	Image	Image (PlantVillage) 1 3		9300	2500 CNN	LSTM 90%
	99.02%		Corn stunt disease
Virus	Rice tungro disease	Saha and Ahsan. ^[57]	Stewart wilt	Barley Yellow Dwarf,
Virus
	450			CNN
Xu et al. ^[67]	2021	2021 Image	Image (PlantVillage) 3	9 276	2500 CNN	TCI-ALEXN 91.47%
	99.18%		Yellow dwarf	Chen et al. ^[58]	2020
Tian ^[68]		2019Image			15	500	DenseNet	94.07%
	Image (PlantVillage)		9	2500	Bacterial stalk rot
Bacterial leaf strip
Virus	Leaf fleek	Virus	Barley Stripe Mosaic,

		Wheat Streak Mosaic
Mosaic		Aphids, Stink Bugs,
Yellow dwarf		Cereail Leaf Beetle,
	89.9%
Tagel et al. ^[48]	2021	Image	3	1500	VGG19	99.38%	Mosaic
Hussain et al. ^[49]	2018	Image	4	8828		Insect	Thrips,
	Hessian Fly, Wireworms,
	Mites
Nematode	Seed Gall Nematode
	Cereal Cyst Nematode
	Root Knot Nematode

2.3. Rice

Rice is the second most-produced cereal crop in the world ^[28]. It is the main source of food for billions of people in the world and is one of the primary food sources for the majority of people in Asia ^[29] with around 500 metric tons ^[30] of rice milled every year. Rice is susceptible to a variety of disease-causing pathogens that attack the leaf, the seed, the stem, and the root ^[31], some are given in Table 3.

AlexNet

84.54%

Powdery mildew, Stem rust, Glume blotch

golden stripe

Table 7.

3. Machine Learning-Based Cereal Crop Disease Detection

3.1. Machine Learning in Wheat Disease Detection

Bao et al. ^[38] applied elliptical-maximum margin criterion metric learning to the identification and severity estimation of powdery mildew and stripe wheat disease types. The researchers choose the E-MMC algorithm since it is better suited to finding nonlinear transformations in patterns, and their results show that it achieved superior results when compared to the SVM algorithm. For testing their algorithm, the researchers prepared a dataset from farms around the province of Beijing. In total, they collected 360 images. Disease spot segmentation was performed by using the Otsu thresholding algorithm and feature extraction using HSV histogram, Color moments for color attributes, and LBP and Gabor for texture attributes.

Identification of various wheat diseases using hyper-spectral image data were performed by ^{[39][40][41][42]}. Identification of wheat powdery mildew disease using linear regression and an SVM (Figure 1) classifier on hyper-spectral data ranging from 656 nm to 784 nm was implemented by Huang et al. ^[40]. The authors employed the Relief-F algorithm to identify the best spectral bands and evaluation of the SVM algorithm was performed by k-fold cross-validation. In addition, Huang et al. ^[41] proposed an SVM-based detection of Fusarium Head Blight on wheat heads using hyperspectral imagery. Here, Fishers Linear Discrimination (FLD) was implemented for dimensionality reduction. An in-field detection of yellow rust and fusarium head blight in wheat-based on the ground and UAV-based platforms was discussed by Bohnenkamp et al. ^[43] (Figure 2) and Xiao et al. ^[44].

Figure 1. Flow chart for hyper-spectral image data analysis and processing for wheat rust detection ^[40].

Figure 2.



VGG16

	96.8%
Jiang et al. ^[50]	2017	Image	6	9230	VGG-FCN	97.95%
kamrul et al. ^[59]	2019	Image	2	284	InceptionV3	99%	Azadbakht et al. ^[51]	2019	Hyper-spectral	2	284	v-SVR	0.99R²
Hasan et al. ^[60]	2019	Image	9	1080	InceptionV3	97.5%	Huang et al. ^[40
Sethy et al. ^[61]	2020	Image	11	5932	SVM	98.38%
Zhou et al. ^[62]	2019	Image	3

References

Debelee, T.G.; Kebede, S.R.; Schwenker, F.; Shewarega, Z.M. Deep Learning in Selected Cancers’ Image Analysis—A Survey. J. Imaging 2020, 6, 121.
Rufo, D.D.; Debelee, T.G.; Ibenthal, A.; Gachena, W.G. Diagnosis of diabetes mellitus using gradient boosting machine (LightGBM). Diagnostics 2021, 11, 1714.
Debelee, T.G.; Amirian, M.; Ibenthal, A.; Palm, G.; Schwenker, F. Classification of mammograms using convolutional neural network based feature extraction. In Proceedings of the International Conference on Information and Communication Technology for Develoment for Africa, Bahir Dar, Ethiopia, 25–27 September 2017; Springer: Cham, Switzerland, 2017; pp. 89–98.
Rufo, D.D.; Debelee, T.G.; Gachena, W.G. A Hybrid Machine Learning Model Based on Global and Local Learner Algorithms for Diabetes Mellitus Prediction. J. Biomim. Biomater. Biomed. Eng. 2022, 54, 65–88.
Biratu, E.S.S.; Schwenker, F.; Debelee, T.G.G.; Kebede, S.R.R.; Negera, W.G.G.; Molla, H.T.T. Enhanced Region Growing for Brain Tumor MR Image Segmentation. J. Imaging 2021, 7, 22.
Debelee, T.G.; Schwenker, F.; Ibenthal, A.; Yohannes, D. Survey of deep learning in breast cancer image analysis. Evol. Syst. 2020, 11, 143–163.
Debelee, T.G.; Gebreselasie, A.; Schwenker, F.; Amirian, M.; Yohannes, D. Classification of mammograms using texture and cnn based extracted features. J. Biomim. Biomater. Biomed. Eng. 2019, 42, 79–97.
Rahimeto, S.; Debelee, T.G.; Yohannes, D.; Schwenker, F. Automatic pectoral muscle removal in mammograms. Evol. Syst. 2019, 12, 519–526.
Kebede, S.R.; Debelee, T.G.; Schwenker, F.; Yohannes, D. Classifier Based Breast Cancer Segmentation. J. Biomim. Biomater. Biomed. Eng. 2020, 47, 41–61.
Gelana, F.; Yadav, A. Firearm detection from surveillance cameras using image processing and machine learning techniques. In Smart Innovations in Communication and Computational Sciences; Springer: Singapore, 2019; pp. 25–34.
Debelee, T.G.; Schwenker, F.; Kebede, S.R.; Yohannes, D. Evaluation of modified adaptive k-means segmentation algorithm. Comput. Vis. Media 2019, 5, 347–361.
Waldamichael, F.G.; Debelee, T.G.; Ayano, Y.M. Coffee disease detection using a robust HSV color-based segmentation and transfer learning for use on smartphones. Int. J. Intell. Syst. 2011.
Kibru, Y.; Debelee, T. Detection of Bacterial Wilt on Enset Crop Using Deep Learning Approach. Int. J. Eng. Res. Afr. 2020, 51, 131–146.
Chlingaryan, A.; Sukkarieh, S.; Whelan, B. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Comput. Electron. Agric. 2018, 151, 61–69.
Nturambirwe, J.F.I.; Opara, U.L. Machine learning applications to non-destructive defect detection in horticultural products. Biosyst. Eng. 2020, 189, 60–83.
Rasti, S.; Bleakley, C.J.; Holden, N.; Whetton, R.; Langton, D.; O’Hare, G. A Survey of High Resolution Image Processing Techniques for Cereal Crop Growth Monitoring. Inf. Process. Agric. 2021.
Sharma, R.; Kamble, S.S.; Gunasekaran, A.; Kumar, V.; Kumar, A. A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Comput. Oper. Res. 2020, 119, 104926.
Virnodkar, S.S.; Pachghare, V.K.; Patil, V.; Jha, S.K. Remote sensing and machine learning for crop water stress determination in various crops: A critical review. Precis. Agric. 2020, 21, 1121–1155.
Stubbs, R.; Prescott, J.; Saari, E.; Dubin, H. Cereal Disease Methodology Manual. 1986. Available online: http://hdl.handle.net/10883/3997 (accessed on 21 February 2022).
Strange, R.N.; Scott, P.R. Plant disease: A threat to global food security. Annu. Rev. Phytopathol. 2005, 43, 83–116.
Różewicz, M.; Wyzińska, M.; Grabiński, J. The Most Important Fungal Diseases of Cereals—Problems and Possible Solutions. Agronomy 2021, 11, 714.
Schaad, N.W.; Frederick, R.D.; Shaw, J.; Schneider, W.L.; Hickson, R.; Petrillo, M.D.; Luster, D.G. Advances in molecular-based diagnostics in meeting crop biosecurity and phytosanitary issues. Annu. Rev. Phytopathol. 2003, 41, 305–324.
Barton, S. CEREALS | Grain Defects. In Encyclopedia of Grain Science; Wrigley, C., Ed.; Elsevier: Oxford, UK, 2004; pp. 223–228.
Shewry, P.R. Wheat. J. Exp. Bot. 2009, 60, 1537–1553.
Duveiller, E.; Singh, R.; Singh, P.; Dababat, A.; Mezzalama, M. Wheat Diseases and Pests: A Guide for Field Identification. 2012. Available online: http://hdl.handle.net/10883/1115 (accessed on 21 February 2022).
Orhun, G.E. Maize for life. Int. J. Food Sci. Nutr. Eng. 2013, 3, 13–16.
Subedi, S. A review on important maize diseases and their management in Nepal. J. Maize Res. Dev. 2015, 1, 28–52.
Van Nguyen, N.; Ferrero, A. Meeting the Challenges of Global Rice Production. Paddy Water Environ. 2006, 4, 1–9.
Yoshida, S. Fundamentals of Rice Crop Science; International Rice Research Institute: Los Baños, Philippines, 1981.
Muthayya, S.; Sugimoto, J.D.; Montgomery, S.; Maberly, G.F. An overview of global rice production, supply, trade, and consumption. Ann. N. Y. Acad. Sci. 2014, 1324, 7–14.
Phadikar, S.; Sil, J.; Das, A.K. Rice diseases classification using feature selection and rule generation techniques. Comput. Electron. Agric. 2013, 90, 76–85.
Bekele, B.; Alemayehu, F.; Lakew, B. Food barley in Ethiopia. In Food Barley: Importance, Uses, and Local Knowledge; ICARDA: Aleppo, Syria, 2005; pp. 53–82.
Mulatu, B.; Grando, S. Barley Research and Development in Ethiopia; EIAR: Addis Ababa, Ethiopia, 2011.
Paulitz, T.C.; Steffenson, B.J. Biotic stress in barley: Disease problems and solutions. In Barley Production, Improvement, and Uses; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2011; pp. 307–354.
Ramatoulaye, F.; Mady, C.; Fallou, S. Production and use sorghum: A literature review. J. Nutr. Health Food Sci. 2016, 4, 1–4.
Rooney, W.L. Sorghum. In Cellulosic Energy Cropping Systems; John Wiley & Sons: Hoboken, NJ, USA, 2014; pp. 109–129.
Pande, S.; Harikrishnan, R.; Alegbejo, M.; Mughogho, L.; Karunakar, R.; Ajayi, O. Prevalence of sorghum diseases in Nigeria. Int. J. Pest Manag. 1993, 39, 297–303.
Bao, W.; Zhao, J.; Hu, G.; Zhang, D.; Huang, L.; Liang, D. Identification of wheat leaf diseases and their severity based on elliptical-maximum margin criterion metric learning. Sustain. Comput. Inform. Syst. 2021, 30, 100526.
Guo, A.; Huang, W.; Ye, H.; Dong, Y.; Ma, H.; Ren, Y.; Ruan, C. Identification of wheat yellow rust using spectral and texture features of hyperspectral images. Remote Sens. 2020, 12, 1419.
Huang, L.; Ding, W.; Liu, W.; Zhao, J.; Huang, W.; Xu, C.; Zhang, D.; Liang, D. Identification of wheat powdery mildew using in-situ hyperspectral data and linear regression and support vector machines. J. Plant Pathol. 2019, 101, 1035–1045.
Huang, L.; Wu, Z.; Huang, W.; Ma, H.; Zhao, J. Identification of fusarium head blight in winter wheat ears based on fisher’s linear discriminant analysis and a support vector machine. Appl. Sci. 2019, 9, 3894.
Khan, I.H.; Liu, H.; Li, W.; Cao, A.; Wang, X.; Liu, H.; Cheng, T.; Tian, Y.; Zhu, Y.; Cao, W.; et al. Early Detection of Powdery Mildew Disease and Accurate Quantification of Its Severity Using Hyperspectral Images in Wheat. Remote Sens. 2021, 13, 3612.
Bohnenkamp, D.; Behmann, J.; Mahlein, A.K. In-field detection of yellow rust in wheat on the ground canopy and UAV scale. Remote Sens. 2019, 11, 2495.
Xiao, Y.; Dong, Y.; Huang, W.; Liu, L.; Ma, H. Wheat Fusarium Head Blight Detection Using UAV-Based Spectral and Texture Features in Optimal Window Size. Remote Sens. 2021, 13, 2437.
Sood, S.; Singh, H. An implementation and analysis of deep learning models for the detection of wheat rust disease. In Proceedings of the 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, 3–5 December 2020; pp. 341–347.
Mukhtar, H.; Khan, M.Z.; Khan, M.U.G.; Younis, H. Wheat Disease Recognition through One-shot Learning using Fields Images. In Proceedings of the 2021 International Conference on Artificial Intelligence (ICAI), Islamabad, Pakistan, 5–7 April 2021; pp. 229–233.
Kumar, D.; Kukreja, V. N-CNN Based Transfer Learning Method for Classification of Powdery Mildew Wheat Disease. In Proceedings of the 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 5–7 March 2021; pp. 707–710.
Aboneh, T.; Rorissa, A.; Srinivasagan, R.; Gemechu, A. Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU Infrastructure. Technologies 2021, 9, 47.
Hussain, A.; Ahmad, M.; Mughal, I.A. Automatic Disease Detection in Wheat Crop using Convolution Neural Network. In Proceedings of the 4th International Conference on Next, Generation Computing, Dehradun, India, 21–22 November 2018.
Lu, J.; Hu, J.; Zhao, G.; Mei, F.; Zhang, C. An in-field automatic wheat disease diagnosis system. Comput. Electron. Agric. 2017, 142, 369–379.
Azadbakht, M.; Ashourloo, D.; Aghighi, H.; Radiom, S.; Alimohammadi, A. Wheat leaf rust detection at canopy scale under different LAI levels using machine learning techniques. Comput. Electron. Agric. 2019, 156, 119–128.
Chen, J.; Zhang, D.; Zeb, A.; Nanehkaran, Y.A. Identification of rice plant diseases using lightweight attention networks. Expert Syst. Appl. 2021, 169, 114514.
Wang, Y.; Wang, H.; Peng, Z. Rice diseases detection and classification using attention based neural network and bayesian optimization. Expert Syst. Appl. 2021, 178, 114770.
Liang, W.j.; Zhang, H.; Zhang, G.f.; Cao, H.x. Rice blast disease recognition using a deep convolutional neural network. Sci. Rep. 2019, 9, 1–10.
Rahman, M.A.; Shoumik, M.S.N.; Rahman, M.M.; Hena, M.H. Rice Disease Detection Based on Image Processing Technique. In Smart Trends in Computing and Communications: Proceedings of SmartCom 2020; Springer: Berlin/Heidelberg, Germany, 2021; pp. 135–145.
Ramesh, S.; Vydeki, D. Rice Disease Detection and Classification Using Deep Neural Network Algorithm. In Micro-Electronics and Telecommunication Engineering; Springer: Singapore, 2020; pp. 555–566.
Saha, S.; Ahsan, S.M.M. Rice Disease Detection using Intensity Moments and Random Forest. In Proceedings of the 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), Dhaka, Bangladesh, 27–28 February 2021; pp. 166–170.
Chen, J.; Zhang, D.; Nanehkaran, Y.A.; Li, D. Detection of rice plant diseases based on deep transfer learning. J. Sci. Food Agric. 2020, 100, 3246–3256.
Kamrul, M.H.; Paul, P.; Rahman, M. Machine vision based rice disease recognition by deep learning. In Proceedings of the 2019 22nd International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 18–20 December 2019; pp. 1–6.
Hasan, M.J.; Mahbub, S.; Alom, M.S.; Nasim, M.A. Rice disease identification and classification by integrating support vector machine with deep convolutional neural network. In Proceedings of the 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 3–5 May 2019; pp. 1–6.
Sethy, P.K.; Barpanda, N.K.; Rath, A.K.; Behera, S.K. Deep feature based rice leaf disease identification using support vector machine. Comput. Electron. Agric. 2020, 175, 105527.
Zhou, G.; Zhang, W.; Chen, A.; He, M.; Ma, X. Rapid detection of rice disease based on FCM-KM and faster R-CNN fusion. IEEE Access 2019, 7, 143190–143206.
Agarwal, R.; Sharma, H. Enhanced Convolutional Neural Network (ECNN) for Maize Leaf Diseases Identification. In Smart Innovations in Communication and Computational Sciences; Springer: Berlin/Heidelberg, Germany, 2021; pp. 297–307.
Sibiya, M.; Sumbwanyambe, M. A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering 2019, 1, 119–131.
Barman, U.; Sahu, D.; Barman, G.G. A Deep Learning Based Android Application to Detect the Leaf Diseases of Maize. In Proceedings of the Sixth International Conference on Mathematics and Computing; Springer: Berlin/Heidelberg, Germany, 2021; pp. 275–286.
Hasan, M.J.; Alom, M.S.; Dina, U.F.; Moon, M.H. Maize Diseases Image Identification and Classification by Combining CNN with Bi-Directional Long Short-Term Memory Model. In Proceedings of the 2020 IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh, 5–7 June 2020; pp. 1804–1807.
Xu, Y.; Zhao, B.; Zhai, Y.; Chen, Q.; Zhou, Y. Maize Diseases Identification Method Based on Multi-Scale Convolutional Global Pooling Neural Network. IEEE Access 2021, 9, 27959–27970.
Tian, J.; Zhang, Y.; Wang, Y.; Wang, C.; Zhang, S.; Ren, T. A method of corn disease identification based on convolutional neural network. In Proceedings of the 2019 12th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, 14–15 December 2019; Volume 1, pp. 245–248.