With limited retrieval of reserves and restricted capability in plant pathology, automation of processes becomes essential. All over the world, farmers are struggling to prevent various harm from bacteria or pathogens such as viruses, fungi, worms, protozoa, and insects. Deep learning is currently widely used across a wide range of applications, including desktop, web, and mobile. In this study, the authors attempt to implement the function of AlexNet modification architecture-based CNN on the Android platform to predict tomato diseases based on leaf image. A dataset with of 18,345 training data and 4,585 testing data was used to create the predictive model. The information is separated into ten labels for tomato leaf diseases, each with 64 × 64 RGB pixels. The best model using the Adam optimizer with a realizing rate of 0.0005, the number of epochs 75, batch size 128, and an uncompromising cross-entropy loss function, has a high model accuracy with an average of 98%, a strictness rate of 0.98, a recall value of 0.99, and an F1-count of 0.98 with a loss of 0.1331, so that the classification results are good and very precise.

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AlexNet Convolutional Neural Network for Disease Detection and Classification of Tomato Leaf

Assessment of state-of-the-art deep learning based citrus disease detection techniques using annotated optical leaf images

Rice leaf infections are a common hazard to rice production, affecting many farmers all over the world. Early detection and treatment of rice leaf infection are critical for promoting healthy rice plant growth and ensuring adequate supply for the fast-growing population. Computer-assisted rice leaf disease diagnoses are hampered due to strong image backgrounds. Popular Convolutional Neural Network (CNN) architecture extracts the features from images and diagnoses the disease to address the issues above. However, this method is best suitable for segmented images and gives low accuracy with real-time images. In this case, the Internet of Things is a paradigm shift that collects agro-meteorological information that effectively helps diagnose rice diseases. Motivated by the usefulness of CNN models and agricultural IoT, a novel multimodal data fusion framework named Rice-Fusion is proposed to diagnose rice disease. Rice disease diagnosis based on a single modality may not be accurate, and hence the fusion of heterogeneous modalities is essential for robust and reliable disease diagnosis. This gives a new dimension to the domain of rice disease diagnosis. The dataset was collected manually with 3200 rice health category samples using two modalities, namely agro-meteorological sensors and a camera. The Rice-Fusion framework initially extracts the numerical features from agro-meteorological data collected from sensors. Next, it extracts the visual features from the captured rice images. These extracted features are further fused using a concatenation layer followed by a dense layer, which provides single output for diagnosing the rice disease. The testing accuracy of Rice-Fusion is 95.31% as opposed to other unimodal framework accuracies of 82.03% and 91.25% based on CNN and Multi-Layer Perceptron (MLP) architectures, respectively. Experimental results analysis demonstrates that the proposed Rice-Fusion multimodal data fusion framework outperforms the outcome of unimodal frameworks.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09672157.pdf

Rice-Fusion: A Multimodality Data Fusion Framework for Rice Disease Diagnosis

Rapid improvements in deep learning (DL) techniques have made it possible to detect and recognize objects from images. DL approaches have recently entered various agricultural and farming applications after being successfully employed in various fields. Automatic identification of plant diseases can help farmers manage their crops more effectively, resulting in higher yields. Detecting plant disease in crops using images is an intrinsically difficult task. In addition to their detection, individual species identification is necessary for applying tailored control methods. A survey of research initiatives that use convolutional neural networks (CNN), a type of DL, to address various plant disease detection concerns was undertaken in the current publication. In this work, we have reviewed 100 of the most relevant CNN articles on detecting various plant leaf diseases over the last five years. In addition, we identified and summarized several problems and solutions corresponding to the CNN used in plant leaf disease detection. Moreover, Deep convolutional neural networks (DCNN) trained on image data were the most effective method for detecting early disease detection. We expressed the benefits and drawbacks of utilizing CNN in agriculture, and we discussed the direction of future developments in plant disease detection.

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Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review

Rice leaf infections are a common hazard to rice production, affecting many farmers all over the world. Early detection and treatment of rice leaf infection are critical for promoting healthy rice plant growth and ensuring adequate supply for the fast-growing population. Computer-assisted rice leaf disease diagnoses are hampered due to strong image backgrounds. Popular Convolutional Neural Network (CNN) architecture extracts the features from images and diagnoses the disease to address the issues above. However, this method is best suitable for segmented images and gives low accuracy with real-time images. In this case, the Internet of Things is a paradigm shift that collects agro-meteorological information that effectively helps diagnose rice diseases. Motivated by the usefulness of CNN models and agricultural IoT, a novel multimodal data fusion framework named Rice-Fusion is proposed to diagnose rice disease. Rice disease diagnosis based on a single modality may not be accurate, and hence the fusion of heterogeneous modalities is essential for robust and reliable disease diagnosis. This gives a new dimension to the domain of rice disease diagnosis. The dataset was collected manually with 3200 rice health category samples using two modalities, namely agro-meteorological sensors and a camera. The Rice-Fusion framework initially extracts the numerical features from agro-meteorological data collected from sensors. Next, it extracts the visual features from the captured rice images. These extracted features are further fused using a concatenation layer followed by a dense layer, which provides single output for diagnosing the rice disease. The testing accuracy of Rice-Fusion is 95.31&#x0025; as opposed to other unimodal framework accuracies of 82.03&#x0025; and 91.25&#x0025; based on CNN and Multi-Layer Perceptron (MLP) architectures, respectively. Experimental results analysis demonstrates that the proposed Rice-Fusion multimodal data fusion framework outperforms the outcome of unimodal frameworks. 

The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems.

https://www.mdpi.com/2079-9292/10/12/1388/pdf

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

The timely identification of plant diseases prevents the negative impact on crops. Convolutional neural network, particularly deep learning is used widely in machine vision and pattern recognition task. Researchers proposed different deep learning models in the identification of diseases in plants. However, the deep learning models require a large number of parameters, and hence the required training time is more and also difficult to implement on small devices. In this paper, we have proposed a novel deep learning model based on the inception layer and residual connection. Depthwise separable convolution is used to reduce the number of parameters. The proposed model has been trained and tested on three different plant diseases datasets. The performance accuracy obtained on plantvillage dataset is 99.39%, on the rice disease dataset is 99.66%, and on the cassava dataset is 76.59%. With fewer number of parameters, the proposed model achieves higher accuracy in comparison with the state-of-art deep learning models.

Plant Disease Identification Using a Novel Convolutional Neural Network

Various plant diseases are major threats to agriculture. For timely control of different plant diseases in effective manner, automated identification of diseases are highly beneficial. So far, different techniques have been used to identify the diseases in plants. Deep learning is among the most widely used techniques in recent times due to its impressive results. In this work, we have proposed two methods namely shallow VGG with RF and shallow VGG with Xgboost to identify the diseases. The proposed model is compared with other hand-crafted and deep learning-based approaches. The experiments are carried on three different plants namely corn, potato, and tomato. The considered diseases in corns are Blight, Common rust, and Gray leaf spot, diseases in potatoes are early blight and late blight, and tomato diseases are bacterial spot, early blight, and late blight. The result shows that our implemented shallow VGG with Xgboost model outperforms different deep learning models in terms of accuracy, precision, recall, f1-score, and specificity. Shallow Visual Geometric Group (VGG) with Xgboost gives the highest accuracy rate of 94.47% in corn, 98.74% in potato, and 93.91% in the tomato dataset. The models are also tested with field images of potato, corn, and tomato. Even in field image the average accuracy obtained using shallow VGG with Xgboost are 94.22%, 97.36%, and 93.14%, respectively.

Plant Disease Identification Using Shallow Convolutional Neural Network

Plant plays an important role in earth’s atmosphere as well as in industry and medicinal field. Therefore, it is important to analyze and correctly identify the plant species using plant leaf. In this paper, we have implemented auto-encoder and auto-encoder with convolution neural network (AE-CNN) for plant identification from leaf. Experimental result demonstrates that more accurate results can be obtained in classification using AE-CNN. The proposed technique is also compared with other state-of-art existing techniques, and it is found our methods are having an accuracy of 96%. In this work, we have also used several types of data augmentation techniques, namely sample-wise and feature-wise pixel standardization, ZCA transform, rotation, random flip and random shift. The proposed method is very effective in identification of leaves with varying size and orientation.

A Hybridized Auto-encoder and Convolution Neural Network-Based Model for Plant Identification

Early detection and identification of plant diseases from leaf images using machine learning is an important and challenging research area in the field of agriculture. There is a need for such kinds of research studies in India because agriculture is one of the main sources of income which contributes seventeen percent of the total gross domestic product (GDP). Effective and improved crop products can increase the farmer’s profit as well as the economy of the country. In this paper, a comprehensive review of the different research works carried out in the field of plant disease detection using both state-of-art, handcrafted-features- and deep-learning-based techniques are presented. We address the challenges faced in the identification of plant diseases using handcrafted-features-based approaches. The application of deep-learning-based approaches overcomes the challenges faced in handcrafted-features-based approaches. This survey provides the research improvement in the identification of plant diseases from handcrafted-features-based to deep-learning-based models. We report that deep-learning-based approaches achieve significant accuracy rates on a particular dataset, but the performance of the model may be decreased significantly when the system is tested on field image condition or on different datasets. Among the deep learning models, deep learning with an inception layer such as GoogleNet and InceptionV3 have better ability to extract the features and produce higher performance results. We also address some of the challenges that are needed to be solved to identify the plant diseases effectively.

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Sk Mahmudul Hassan

Papers

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

Plant Disease Identification Using a Novel Convolutional Neural Network

Plant Disease Identification Using Shallow Convolutional Neural Network

A Hybridized Auto-encoder and Convolution Neural Network-Based Model for Plant Identification

A Survey on Different Plant Diseases Detection Using Machine Learning Techniques