A pest control is therefore crucial to reduce the crop loss. If they are left untreated even in a small area, they can quickly spread out other healthy area or nearby countries. Pest invasions, however, are a big threat to the growth crops which cause the crop loss and economic consequences. Currently, farmers are facing the challenge to produce sufficient crops for expanding human population and growing in economy, while maintaining the quality of agriculture products. It is the source of not only food supply, but also providing raw materials for other industries, e.g. To this day, agriculture still remains very important and plays considerable role to support our daily life and economy in most countries. EfficientNet-B0 has consumed the lowest time in recognizing unseen images compared to others. AlexNet and VGG16 have achieved 87.22% and 93.79% accuracy, respectively. AlexNet, VGG16, and EfficientNet-B0 have used in this study using the transfer learning approach, and EfficientNet-B0 has achieved the highest accuracy of 98.63% under the test set of 5250 images. Using image augmentation techniques, a dataset of 41250 images of different inflammatory foods have generated from 10000 images. This paper addresses a rapid food recognition approach to assist psoriasis patients to recognize fifteen highly inflammatory foods. Inflammatory foods increase inflammation rapidly, patients can easily control inflammation to enhance the quality of life by eliminating these foods from their everyday diet.
#OKRA PLANT IMAGES SKIN#
Psoriasis affects people of all ages around the world and causes inflammation on the skin with significant systemic disability and illness. A machine-vision-based technique, i.e., the Convolutional Neural Network (CNN), has been used in this research work.ĭeep learning becomes the spotlight in computer vision based recognition approaches in recent years. Our research focuses on identifying litchi leaf diseases by employing sophisticated image processing technologies to ensure the freshness of the leaves. Early leaf disease detection can help in keeping the leaves disease-free, as well as the plants and fruits. A good plant's growth is dependent on its leaves. Thanks to the huge advancement in technology, rare diseases can now be detected with the use of the right technologies. In many cases, locals fail to even detect rare diseases. To deal with this issue, quick detection of the disease and correct treatment or recuperation is required. Native fruits are contracting many types of new diseases, and the magnitude of the problem is increasing alarmingly. Owing to numerous diseases, both the quality and quantity of fruits are not meeting expectations. Fruits are adored by the people of this country, and farmers love growing fruits. Therefore, it's more necessary to grow our crops and fruits and boost their harvests. Agriculture is also a big part of the economy of the country.
InceptionResNetV2 showed significant performance compared to others, achieved 98.73% and 98.16% accuracy under the training set of 99808 images, and the test set of 6236 images of the used dataset, respectively.Īgriculture is the primary source of income for the majority of the population in Bangladesh. Afterwards, state-of-the-art deep learning models such as InceptionResNetV2, Xception, ResNet50, MobileNetV2, VGG16, and AlexNet were utilized with the transfer learning approach. At first, a dataset of fifteen classes is generated from 12476 collected images using nine image augmentation techniques which contains 124760 images of okra plant diseases and pests. This paper addresses an early recognition approach for controlling the disease and pest spread to ensure quality production of okra.
Okra plant diseases and pests cause intense monetary losses to the growing okra industry, but their accurate and rapid identification remains troublesome due to the lack of efficient approaches. The issue of effective plant disease and pest prevention is compactly connected to the issues of sustainable agronomics and climate change.
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