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

Thermal performance assessment of building(s) is an essential process for optimal energy management, heat-loss evaluation, and energy audit applications. Such an assessment can help foresee the requirements for future intervention(s) and aid in benchmarking energy performance. This paper provides a review of several thermal performance assessment techniques and a broad classification based on measurement types, methods, and applications. Moreover, the article provides a comprehensive survey of various quantitative indices utilized for practical heat-loss assessment of building elements. This paper’s unique contribution is the proposed three-layer framework that details the handling and processing of UAS-based thermal imagery for heat loss quantification. Primarily, the novelty of this work lies in the application of an instance segmentation technique (Mask R–CNN) to compute the thermal transmittance values (e.g., U-values) for various objects (e.g., doors, walls, windows, and facades). To the best of our knowledge, this research work is first-of-its-kind using a sizeable thermal data repository (e.g. 100,000 augmented images). Multiple standard U-values are analyzed for windows and walls and compared with The American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE) building standards. The preliminary results of Mask-RCNN from over 100,000 trained (including augmented) images from multiple campus buildings yield the following performance metrics: 1) provides an Average Precision (AP) of 0.67 (windows) and 0.46 (facades); and 2) Intersection of Union (IoU) of 0.05 (windows) and 0.5 (facades) respectively. Moreover, the U-values are consistently close enough to the ASHRAE standards in distinguishing window types (e.g. 0.77 for single-pane windows and 0.38 for double-pane windows).

Estimating surface temperature from thermal imagery of buildings for accurate thermal transmittance (U-value): A machine learning perspective

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.

Piyush Singh

Papers

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

Modified Convolutional Neural Network Architecture Analysis for Facial Emotion Recognition