Hi, I'm Ashen, an Undergraduate from Gampaha.
Selected work
Inspect your crops with the planter by simply taking a photo of your crops to check are those healthy or not. get more knowledge about diseases affected to your crops to much more effective disease control, learn effective organic, non-organic disease controlling methods and cultural practices, without damaging plant harvest at the right time and improve your cultivation knowledge with our cultivation tips.
The optimal way to predict the medical cost of patients based on their historical data such as age, gender children, smoking habits and their region. of course, this is a regression problem so linear, non-linear and ensemble methods are used to choose the best model to predict with less variance and with higher accuracy possible.
Publications
2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS) · Sep 20, 2023
In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networks.
2022 International Research Conference on Smart Computing and Systems Engineering (SCSE) · Oct 4, 2022
Following better agricultural practices is the key to catering for the ever-increasing food demand. While new technologies have been adapted over the years, there is still a need for effective plant disease recognition systems because of the existence of harmful plant diseases that can spread rapidly. Effective and early recognition of plant diseases is vital to minimize the damage to crops and hence can save the farmers from potential loss. It is also important for many countries to maintain economic stability, especially for countries that completely rely on agriculture. In the past, many traditional and deep learning-based approaches have been proposed for plant disease recognition. While traditional approaches need insightful domain expertise, deep learning-based approaches require large sets of labeled data. Further, most of the existing methods fail to meet benchmark performances in terms of recognition accuracy. Therefore, in this study, a novel deep hybrid architecture is proposed to perform plant disease recognition from plant leave images. The Google Inception and ResNet architectures are utilized as the core networks to construct the proposed network. The proposed framework is evaluated on a newly constructed dataset with a large sample size. The comparative analysis reveals that the proposed approach can outperform other state-of-the-art deep networks.
latest blogs
I already face this question several times, which "what is machine learning?" most of the time my response was too much complicated and a very long one. I experienced people who ask that question getting a bit confused or didn't understand at all. I think that happens because I used many technical terms to explain the concept. so people without any technical background getting confused. with this post, I try to explain machine learning as much as simple I can.
what will decide whether our model performer well or perform horribly? hmm... well, there are a few things that are effect models. data quality, model complexity, hyperparameters etc. so what about model complexity? yeah.. it's. before talking about model complexity, we should have a few things: train set, validation set, bias, variance, overfitting, and underfitting. let's talk about them first.
supervise learning can be divided into future two parts which are regression and classification. we use regression to predict numerical values with historical data. likewise, classification techniques are used to classify data using their shared qualities or characteristics. in machine learning we can do this classification using several algorithms in this post I hope to build a logistic regression model to use in binary classification. binary means there are only two possible outcomes like head or tail, right or left, or yes/no.