203 research outputs found
Performance Optimization of Big Data Applications Using Parameter Tuning of Data Platform Features Through Feature Selection Techniques
No full textAn Indian annotated weed dataset for computer vision tasks in precision farmingMendeley Data
No full textWeed infestations are the major threat for agriculture sector in India, significantly impacting crop productivity. These invasive plants not only attract pests but also compete with crops for essential nutrients, contributing to an estimated 45 % of the annual productivity loss in agriculture. For smallholder farmers, traditional methods such as manual weeding is both labour-intensive and expensive. Heavy reliance on usage of chemical herbicides has led to resistance in several weed species. Emerging technologies such as artificial intelligence and computer vision are transitioning farming sector by automating tasks. The main component for development of these technologies is the availability of datasets. To address this need, a comprehensive MH-Weed16 image dataset is created which consists of total 25,972 images acquired from real fields of Maharashtra region. Dataset includes 16 different weed species, annotated under guidance of agriculture experts. Out of total, dataset contains 7577 samples featuring both crops and weeds, captured from a top view to ensure precise estimation of weed areas. The proposed dataset will serve as a valuable resource for computer vision tasks in precision farming. The objective of this research is to contribute towards integrating technology for weed management strategies, paving the way for sustainable agricultural practices
Deep learning model based on cascaded autoencoders and one‐class learning for detection and localization of anomalies from surveillance videos
No full textAbstract Due to the need for increased security measures for monitoring and safeguarding the activities, video anomaly detection is considered as one of the significant research aspects in the domain of computer vision. Assigning human personnel to continuously check the surveillance videos for finding suspicious activities such as violence, robbery, wrong U‐turns, to mention a few, is a laborious and error‐prone task. It gives rise to the need for devising automated video surveillance systems ensuring security. Motivated by the same, this paper addresses the problem of detection and localization of anomalies from surveillance videos using pipelined deep autoencoders and one‐class learning. Specifically, we used a convolutional autoencoder and a sequence‐to‐sequence long short‐term memory autoencoder in a pipelined fashion for spatial and temporal learning of the videos, respectively. The authors followed the principle of one‐class classification for training the model on normal data and testing it on anomalous testing data. The authors achieved a reasonably significant performance in terms of an equal error rate and the time required for anomaly detection and localization comparable to standard benchmarked approaches, thus, qualifies to work in a near‐real‐time manner for anomaly detection and localization
O Vôo Místico de Attar: Uma Análise Sobre “A Linguagem dos Pássaros”
Get PDFThis article analyzes the work “The Language of the Birds” by the Iranian poet Farid ud-Din Abu Hamad Mohâmmed, best known as Attar. The chosen approach consists first of a brief presentation of the principles of Sufism, of the author and the work itself. This then makes possible to draw a parallel between the symbolisms found in the poem and mystical experience as lived by the Sufis.Este artigo analisa a obra “A Linguagem dos Pássaros” do poeta iraniano Farid ud-Din Abu Hamad Mohâmmed, mais conhecido como Attar. O enfoque acontece a partir de uma breve apresentação dos princípios do sufismo, do autor e da obra em questão, tornando possível traçar um paralelo entre os simbolismos presentes no poema e a vivência da experiência mística pelos sufistas
An Indian UAV and leaf image dataset for integrated crop health assessment of soybean cropMendeley Data
No full textSoybean is an important oilseed crop, rich in protein and oil, often referred to as a ``cash crop'' or ``gold bean'' by Indian farmers. In Maharashtra, soybean cultivation spans over approximately 3.8 million hectares, producing 3.07 million tons, placing the state second in India for overall soybean production. However, despite of its significance, several issues such as weeds, diseases, and pests hamper the overall productivity of soybean. Addressing these challenges faced by soybean growers it is essential to enhance yield and improve the crop's overall potential Currently, the farming sector is transitioning towards Agriculture 5.0, also known as digital farming. This approach utilizes data-driven technologies, such as artificial intelligence and computer vision, to transform the agriculture sector. These technologies enable the automation of several farming tasks. To develop accurate and robust machine learning/deep learning models high quality datasets are needed.With this aim, we have created a comprehensive dataset of soybean crop images affected by diseases and pest attacks from original fields of Maharashtra region located in India. Data acquisition was conducted across two seasons through aerial as well as ground-based approaches. The dataset is enriched with 4 types of diseases and 1 pest attack. The proposed dataset will serve as a valuable resource for training and testing machine learning and deep learning models ,enabling accurate detection and classification of diseases and pests attack damage
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