Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. As a result, designing an automated detection system for citrus plant diseases is important. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, leading us to apply them to the challenge of recognizing citrus fruit and leaf diseases. In this paper, an integrated approach is used to suggest a convolutional neural networks (CNNs) model. The proposed CNN model is intended to differentiate healthy fruits and leaves from fruits/leaves with common citrus diseases such as black spot, canker, scab, greening, and Melanose. The proposed CNN model extracts complementary discriminative features by integrating multiple layers. The CNN model was checked against many state-of-the-art deep learning models on the Citrus and PlantVillage datasets. According to the experimental results, the CNN Model outperforms the competitors in a variety of measurement metrics. The CNN Model has a test accuracy of 94.55 percent, making it a valuable decision support tool for farmers looking to classify citrus fruit/leaf diseases.

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Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

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Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects

Wheat is one of the most common cereal crops in India. Aphids cause extensive damage to the whole wheat plant and lead to high yields loss. The aphid is transmitted on the summer day. Once the aphid is transmitted over the leave, the whole plant leave is damaged. Due to this, got damage whete plant and it also reduce the quality of wheat grain. Due to which the it is necessary to identify each aphid on wheat leaves. Only manual process is there to identify each aphid on wheat leave. Manually identification is a time-consuming and high laborious process. Therefore, the identification of wheat aphids through the Mask RCNN model can easily identify the location of each aphid in individual leave automatically. With the help of a Canon camera, a total of 6500 wheat images have been captured in the Punjab region with temperature 21–24° temperature. The Labelme software is used for the annotation of wheat leaves and wheat aphid. A total of 2300 and 1000 images have been randomly selected for training and testing purposes. The Mask scoring RCNN model is having a network capacity for learning the quality of predicted instance masks. Among all 1221 wheat leaves, a total number of 1021 wheat aphids have been found. The manually annotated ROI was compared to mask scoring ROI for wheat aphid identification and localization. Thus, the Mask scoring RCNN model has been achieved a high F1-score (96.66%) for wheat aphid detection in single wheat leave.

Recognizing Wheat Aphid Disease Using a Novel Parallel Real-Time Technique Based on Mask Scoring RCNN

A variety of fungal and bacterial leaf ailments wreak havoc on the paddy plant in the agricultural field. Early diagnosis of leaf infection can improve the yield of the crop. The modeling of an automatic disease classifier aids farmers in handling the spread of leaf disease in the agricultural field. This work cogitates three paddy leaf diseases (Bacterial blight, leaf smut, and leaf blast) for the creation of an AI-based robust detection and classification model. The dataset is collected from a variety of standard online repositories. GAN-based augmentation technique was used for increasing the size of the dataset. A novel approach to the convolutional neural network is proposed with the combination of augmentation and a CNN model tuner. The performance of CNN is evaluated in terms of accuracy achieved is 98.23\% in the classification process.

GAN based image augmentation for increased CNN performance in Paddy leaf disease classification

The current work focuses on implementing a rice disease detection (RDD) system on hispa rice disease by using real-time rice plant images collected from rice fields of Punjab, trained on a CNN-based deep learning model. The dataset first gets preprocessed using a Matlab tool and then splits up into 70 to 30 ratio which further gets trained and validated on a proposed CNN model results in an accuracy of 94%. The motivation behind the proposed work is due to an unavailability of a system for RDD in case of hispa disease gave rise to a need for an efficient and trained system that will be useful for the detection of rice hispa disease.

Hispa Rice Disease Classification using Convolutional Neural Network

One of the most significant factors is the quality evaluation of agricultural products in supporting their marketability and controlling waste management. To classify the fruits into healthy and defected class, deep learning algorithms have been implemented to perform citrus disease detection. This study aims to use the dense CNN algorithm to detect and provide an effective method for detecting the apparent defects of citrus fruit. Citrus fruit images are collected and put in two classes of good and damaged ones, to recognize and categorize the image dataset. Firstly, a dense CNN model was used without doing preprocessing and data augmentation on 150 images and achieved an accuracy of 67 percent but the proposed model has used data augmentation and pre-processing to enhance the CNN performance and have used 1200 images. Further, the proposed model is compared with the dense model where data augmentation and pre-processing techniques have not been used. The overall accuracy of the proposed model is 89.1%. The results show that techniques of data augmentation and preprocessing have delivered promising insights to estimate citrus fruit’s damages.

A Deep Neural Network based disease detection scheme for Citrus fruits

A retrospective study on handwritten mathematical symbols and expressions: Classification and recognition

In today’s modern era, parking remains a big problem for a lot of people. This problem consumes a person individual’s time by finding the right spot for parking. In the current research, the concept of an automatic parking system using the vehicle license plate or the number plate recognition is discussed. It will improve the process with much less hassle by removing human interaction. It will also lead to advancement in the security of vehicles evading the requirement of a slip or a magnetic card which is used to goes in and out for registering vehicles in a parking place. The researcher uses image processing algorithms to make an entry in the database of the parking automatically. AVNPR (Automatic Vehicle Number Plate Recognition) is used for the identification of the number of plates. Due to noise issues, deep algorithms like CNN (convolutional neural networks), RNN (recurrent neural networks) do not correctly recognize the miss-identification of the numbers in the vehicle plate. This problem is rectified by the authors by using the GAN (Generative adversarial networks) algorithm. GAN helps to create high-resolution images from a single low-resolution image. After applying the GAN, the classification of the vehicle plate is done through CNN. During experimentation, the proposed approach achieves 99.39% recognition accuracy for a vehicle number plate. Hence, the proposed system is suitable for identifying the numbers in the vehicle number plate automatically. Moreover proposed system compared with existing models, it has been found that it has achieved higher accuracy than the other models.

GAN-based synthetic data augmentation for increased CNN performance in Vehicle Number Plate Recognition

Powdery wheat (PW) is one of the most common wheat diseases in northern India. It is the most damaging wheat disease and it is prevalent in April to May season. Several methods of machine learning (ML) and Deep Learning (DL) methods are used to do wheat disease classification. The previous DL techniques have not achieved higher accuracy during PW wheat disease classification. In the current study, 450 wheat images are collected from primary and secondary sources. The normalization technique is used for preprocessing. These normalized preprocessed images are input to CNN. The normalized images increase the training and testing accuracy of CNN. Then, this pre-trained model is applied to the CIAGR images dataset via transfer learning method. During testing with images, CNN achieves 89.9% classification accuracy for PW wheat disease. After these pre-trained model is applied to CIAGR dataset images and achieves 86.5% classification accuracy. Moreover, the result shows that pre-trained NCNN model achieves higher accuracy during transfer learning.

Vinay Kukreja

Papers

A Deep Neural Network based disease detection scheme for Citrus fruits

A retrospective study on handwritten mathematical symbols and expressions: Classification and recognition

GAN-based synthetic data augmentation for increased CNN performance in Vehicle Number Plate Recognition

N-CNN Based Transfer Learning Method for Classification of Powdery Mildew Wheat Disease

Hispa Rice Disease Classification using Convolutional Neural Network