Weed management has a vital role in applications of agriculture domain. One of the key tasks is to identify the weeds after few days of plant germination which helps the farmers to perform early-stage weed management to reduce the contrary impacts on crop growth. Thus, we aim to classify the seedlings of crop and weed species. In this work, we propose a plant seedlings classification using the benchmark plant seedlings dataset. The dataset contains the images of 12 different species where three belongs to plant species and the other nine belongs to weed species. We implement the classification framework using three different deep convolutional neural network architectures, namely ResNet50V2, MobileNetV2 and EfficientNetB0. We train the models using transfer learning and compare the performance of each model on a test dataset of 833 images. We compare the three models and demonstrate that the EfficientNetB0 performs better with an average F1-Score of 96.26% and an accuracy of 96.52%.
Nowadays diabetes mellitus has become the major health problem among the people of all ages. The main problem in this type of dieses is its prediction. It is found that if diabetes mellitus is detected at early stages then it can be cured. So early detection of diabetes mellitus is important. There are different techniques with the help of which early detection of diabetes mellitus is possible. In this paper combination of three different methods used for early detection of diabetes mellitus are given. These three methods are fuzzy system, neural network, case based reasoning. By using combination of all these approaches, it is found that detection of diabetes mellitus at early stages is possible. Benefit of using these systems is that accuracy of prediction rate is higher as compared to other techniques.
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