The use of Deep learning techniques in the field of Marine Science has become popular in recent years. For instance, many works propose the application of instance segmentation neural networks (in particular, Mask R-CNN) for detection and classification of fish in underwater images. The performance of these learning-based approaches depends heavily on the volume of data used for training, which, in the case of instance segmentation models for fish detection, implies that human experts must label and mark the shapes of all the fish appearing in a vast amount of underwater images. This is an enormously time-consuming task that we seek to alleviate in this paper. We propose a training strategy that combines manual and semi-automatic annotations. The latter are obtained in a weakly-supervised manner: the bounding box that contains the fish is manually selected, but its shape is automatically obtained thanks to a pretrained encoder-decoder segmentation network. Several popular architectures for this encoder-decoder network are examined. This strategy permits to reduce drastically the annotation cost for instance segmentation, at the expense of a small drop in performance with respect to the use of fully manual annotations. We show that a balance can be achieved between the segmentation performance and the time used to collect the training data by using the proposed strategy.

Leveraging Bounding Box Annotations for Fish Segmentation in Underwater Images

Deep reinforcement learning for autonomous driving in uncontrolled intersections of Indian roads

We present a cost-effective testbed and advanced software architecture suitable for ADAS applications, which utilizes an onboard GPU for high-performance processing. By implementing an optimized parallel processing method, this innovative architecture can collect and synchronize multi-sensor data, such as camera and radar, with high precision. The software architecture dynamically allocates processing power to each data processing sub-module based on the utilized sensor hardware and optimizes the data flow accordingly. With the aid of parallel processing, the system can effectively reduce the time delay during data acquisition. Indoor and road stereo-camera and dual-radar data acquisition results are presented. Post-processing of the data includes the creation of disparity map depth images and combined radar point clouds identifying departing and approaching vehicles and objects.

A synchronous multi-modal data acquisition and processing system for ADAS applications using an onboard GPU

A research was carried out to isolate and characterize the fungal community present on some ornamental plants grown in Baze  University Abuja Nigeria. Leaf samples showing diseased symptoms were collected from four different ornamental plants in the area  (Indian shot, Canna indica; Plantain lily, Hosta undulata; Elephant ear, Caladium sp; and Song of India, Pleomelere flexa. The diseased portions were cut to form an aliquot. Serial dilutions were carried out and the samples were inoculated on Potato Dextrose Agar and  incubated at 27 oC for 5 days. Fungal colonies formed were counted and sub-cultured. Data obtained for occurrence was analyzed in  frequency and percentages while for enumeration using Analysis of Variance (ANOVA) with Duncan’s New Multiple Range Test was used  to separate significant means at 5% level. The result obtained revealed the presence of ten fungal species represented by eight genera  associated with the plants: Aspergillus niger, Aspergillus parasiticus, Mucor racemosus, M. mucedo, Saccharomyces cerevisiae, Fusarium  oxysporum, Rhizopus stolonifer, Alternaria alternata, Pythium aphanidermatum and Penicillium chrysogenum. Aspergillus niger is the  most predominant species. Elephant ear has the highest fungal loads. Four of the fungal species: A. alternata, Aspergillus parasiticus,  Pythium aphanidermatum and Fusarium oxysporum are potential pathogens while the remaining six species are saprophytes. This  implies that the plants are at risk of losing their aesthetic values as such proper attention need to be given to these plants.     

Fungal Isolation and Characterization from some Ornamental Plants in Baze University Abuja, Nigeria

Advanced CNN Approach for Segmentation of Diseased Areas in Plant Images

Lane detection techniques for self-driving vehicle: comprehensive review

The main objective of this review study is to examine various fungal diseases on angiospermic plants which lower plant productivity and worsen the financial situation of flowers. Angiospermic flora are the most useful and marketable plants, employed for the majority of purposes, contributing to a better environment and spreading positive energy, but these flowering plants are presently damaged by unfavourable environmental conditions. The occurrence of fungal infection suppresses the beauty, growth, production and commercial values of flowers and also affect the living beings by increasing air pollution due to the infected plants. Fungicides, chemicals and molecular techniques are already in used to enhances the yield of crops and growth of plants, but still more research needs to be conducted to find the way for increasing the yield and preventing the plants more.

An Overview on Fungal Diseases in Angiospermic Plants

In plant breeding the presence of a genotype by environment ( GxE ) interaction has a strong impact on cultivation decision making and the introduction of new crop cultivars. The combination of linear and bilinear terms has been shown to be very useful in modelling this type of data. A widely-used approach to identify GxE is the Additive Main Effects and Multiplicative Interaction Effects (AMMI) model. However, as data frequently can be high-dimensional, Markov chain Monte Carlo (MCMC) approaches can be computationally in-feasible. In this article, we consider a variational inference approach for such a model. We derive variational approximations for estimating the parameters and we compare the approximations to MCMC using both simulated and real data. The new inferential framework we propose is on average two times faster whilst maintaining the same predictive performance as MCMC.

Variational inference for additive main and multiplicative interaction effects models

Beyond production: How can diversification enhance farm income in the North-Eastern region of India?

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Lane detection techniques for self-driving vehicle: comprehensive review

An Overview on Fungal Diseases in Angiospermic Plants

Variational inference for additive main and multiplicative interaction effects models

Beyond production: How can diversification enhance farm income in the North-Eastern region of India?