Alternaria leaf blotch, and rust are two common types of apple leaf diseases that severely affect apple yield. A timely and effective detection of apple leaf diseases is crucial for ensuring the healthy development of the apple industry. In general, these diseases are inspected by experienced experts one by one. This is a time-consuming task with unstable precision. Therefore, in this paper, we proposed a LOW-COST, STABLE, HIGH precision apple leaf diseases identification method. This is achieved by employing MobileNet model. Firstly, comparing with general deep learning model, it is a LOW-COST model because it can be easily deployed on mobile devices. Secondly, instead of experienced experts, everyone can finish the apple leaf diseases inspection STABLELY by the help of our algorithm. Thirdly, the precision of MobileNet is nearly the same with existing complicated deep learning models. Finally, in order to demonstrated the effectiveness of our proposed method, several experiments have been carried out for apple leaf diseases identification. We have compared the efficiency and precision with the famous CNN models: i.e. ResNet152 and InceptionV3. Here, the apple disease datasets (including classes: Alternaria leaf blotch and rust leaf) were collected by the agriculture experts in Shaanxi Province, China.

MobileNet Based Apple Leaf Diseases Identification

Lightweight convolutional neural network model for field wheat ear disease identification

Automated emotion recognition: Current trends and future perspectives

Globally, all the major crops are significantly affected by diseases every year, as manual inspection across diverse fields is time-consuming, tedious, and requires expert knowledge. This leads to significant crop loss in different parts of the world. To provide effective solutions, several smart agriculture solutions are deployed for the control of pests and plant diseases using vision-based machine learning techniques. Despite rapid growth in the field, not many methods have been explored for their suitability in real-time applications. Several open challenges need to be addressed for the applicability of machine learning techniques in IoT-based smart agriculture solutions. Starting from data capturing methods and the availability of public datasets, the present paper provides a comprehensive review of vision-based machine learning techniques for plant disease detection. Initially, 1337 articles were selected from various scholarly resources to perform the survey. Based on the saliency of approaches, 148 articles are reviewed in this paper. Interestingly, a significant amount of research in this direction is taken up by Chinese and Indian researchers, and deep learning is the current research trend, as in other fields. The review concludes that a majority of existing methods exhibit their efficacy on public datasets captured mostly in controlled environmental conditions, but their generalization capability for in-field plant disease detection has not been explored. Lightweight CNN-based methods, on the other hand, have been designed for a limited number of diseases only, and are generally trained on small datasets. The scarcity of large-scale, in-field public datasets is one of the major bottlenecks in developing solutions that can work for a wide variety of plant diseases. 

Trends in vision-based machine learning techniques for plant disease identification: A systematic review

Traditional Deep Neural Network (DNN) security is mostly related to the well-known adversarial input example attack. Recently, another dimension of adversarial attack, namely, attack on DNN weight parameters, has been shown to be very powerful. As a representative one, the Bit-Flip-based adversarial weight Attack (BFA) injects an extremely small amount of faults into weight parameters to hijack the executing DNN function. Prior works of BFA focus on un-targeted attack that can hack all inputs into a random output class by flipping a very small number of weight bits stored in computer memory. This paper proposes the first work of targeted BFA based (T-BFA) adversarial weight attack on DNNs, which can intentionally mislead selected inputs to a target output class. The objective is achieved by identifying the weight bits that are highly associated with classification of a targeted output through a class-dependent weight bit ranking algorithm. Our proposed T-BFA performance is successfully demonstrated on multiple DNN architectures for image classification tasks. For example, by merely flipping 27 out of 88 million weight bits of ResNet-18, our T-BFA can misclassify all the images from 'Hen' class into 'Goose' class (i.e., 100 % attack success rate) in ImageNet dataset, while maintaining 59.35 % validation accuracy. Moreover, we successfully demonstrate our T-BFA attack in a real computer prototype system running DNN computation, with Ivy Bridge-based Intel i7 CPU and 8GB DDR3 memory.

T-BFA: Targeted Bit-Flip Adversarial Weight Attack

Disease and pest infection detection in coconut tree through deep learning techniques

Facial expressions are one of the key features of a human being and it can be used to speculate the emotional state at a particular moment. This paper employs the Convolutional Neural Network and Deep Neural Network to develop a facial emotion recognition model that categorizes a facial expression into seven different emotions categorized as Afraid, Angry, Disgusted, Happy, Neutral, Sad and Surprised. This paper compares the performance of two existing deep neural network architectures with our proposed architecture, namely the Venturi Architecture in terms of training accuracy, training loss, testing accuracy and testing loss. This paper uses the Karolinska Directed Emotional Faces dataset which is a set of 4900 pictures of human facial expressions. Two layers of feature maps were used to convolute the features from the images, and then it was passed on to the deep neural network with up to 6 hidden layers. The proposed Venturi architecture shows significant accuracy improvement compared to the modified triangular architecture and the rectangular architecture.

Modified Convolutional Neural Network Architecture Analysis for Facial Emotion Recognition

In recent days, deep learning has been widely used in signal and information processing. Among the deep learning algorithms, Convolution Neural Network (CNN) has been widely used for image recognition and classification because of its architecture, high accuracy and efficiency. This paper proposes a method that uses the CNN on audio samples rather than on the image samples in which the CNN method is usually used to train the model. The one-dimensional audio samples are converted into two-dimensional data that consists of matrix of Mel-Frequency Cepstral Coefficients (MFCCs) that are extracted from the audio samples and the number of windows used in the extraction. This proposed CNN model has been evaluated on the TIDIGITS corpus dataset. The paper analyzes different convolution layer architectures with different number of feature maps in each architecture. The three-layer convolution architecture was found to have the highest accuracy of 97.46% among the other discussed architectures.

Experimental Evaluation of CNN Architecture for Speech Recognition

Objectives: The objective is to identify weakest buses using L-index method and to reduce the maximum value of L-index using Genetic Algorithm so as to maintain the voltage profile Methods/Statistical Analysis: Among the various existing voltage stability indices, L-index is implemented in this paper since it involves simple calculation with high accuracy and reliability Hence on identifying buses with maximum L-index, it becomes essential to minimize the L-index to maintain the system stability Genetic Algorithm is chosen since it is faster and more accurate when implemented for power system problems in particular to optimize the voltage Findings: The variations to be adopted for the control variables which are thus obtained for IEEE 30 bus system by using Genetic Algorithm are highly important to reduce L-index value The voltage profile is improved better when Static Var Compensator is placed at the first weakest bus rather than the improvement in the voltage profile on employing a compensator in the next weakest buses which indicates the importance in identification of weakest buses The significance of weakest buses are still emphasised on considering normal condition and contingency condition which shows that under contingency it is the weakest buses which is much more affected and hence driving the entire system towards voltage collapse Application/Improvements: Hence by identifying the weakest buses using L-index and by minimizing the maximum value of L-index using Genetic Algorithm along with reactive power compensator improves the voltage profile better

Abhishek Verma

Papers

Disease and pest infection detection in coconut tree through deep learning techniques

Modified Convolutional Neural Network Architecture Analysis for Facial Emotion Recognition

Experimental Evaluation of CNN Architecture for Speech Recognition

Minimization of L-index using Genetic Algorithm for Improvement of Voltage profile in Power Systems