Concept explainability for plant diseases classification

Plant diseases remain a considerable threat to food security and agricultural sustainability. Rapid and early identification of these diseases has become a significant concern motivating several studies to rely on the increasing global digitalization and the recent advances in computer vision based on deep learning. In fact, plant disease classification based on deep convolutional neural networks has shown impressive performance. However, these methods have yet to be adopted globally due to concerns regarding their robustness, transparency, and the lack of explainability compared with their human experts counterparts. Methods such as saliency-based approaches associating the network output to perturbations of the input pixels have been proposed to give insights into these algorithms. Still, they are not easily comprehensible and not intuitive for human users and are threatened by bias. In this work, we deploy a method called Testing with Concept Activation Vectors (TCAV) that shifts the focus from pixels to user-defined concepts. To the best of our knowledge, our paper is the first to employ this method in the field of plant disease classification. Important concepts such as color, texture and disease related concepts were analyzed. The results suggest that concept-based explanation methods can significantly benefit automated plant disease identification.Comment: Accepted at VISAPP 202

Još o toksičnosti kadmija - s posebnim osvrtom na nastanak oksidacijskoga stresa i na interakcije s cinkom i magnezijem

Discovered in late 1817, cadmium is currently one of the most important occupational and environmental pollutants. It is associated with renal, neurological, skeletal and other toxic effects, including reproductive toxicity, genotoxicity, and carcinogenicity. There is still much to find out about its mechanisms of action, biomarkers of critical effects, and ways to reduce health risks. At present, there is no clinically efficient agent to treat cadmium poisoning due to predominantly intracellular location of cadmium ions. This article gives a brief review of cadmium-induced oxidative stress and its interactions with essential elements zinc and magnesium as relevant mechanisms of cadmium toxicity. It draws on available literature data and our own results, which indicate that dietary supplementation of either essential element has beneficial effect under condition of cadmium exposure. We have also tackled the reasons why magnesium addition prevails over zinc and discussed the protective role of magnesium during cadmium exposure. These findings could help to solve the problem of prophylaxis and therapy of increased cadmium body burden.Iako je otkriven tek 1817. godine, kadmij je trenutačno jedan od najvažnijih onečišćivača životne i radne sredine. Štetno djeluje na bubrege, živčani sustav, kosti, reproduktivni sistem, a ima i genotoksične i karcinogene efekte. Nužna su dalja istraživanja vezana za mehanizme njegove toksičnosti, biomarkere efekata, kao i načine smanjenja rizika za zdravlje. Osim toga, do danas nije otkriven agens efikasan u terapiji trovanja kadmijem s obzirom na to da je kadmij intracelularni kation. U ovom radu dan je sažet pregled važnih mehanizama toksičnosti kadmija, kao što su nastanak oksidativnog stresa i interakcije s esencijalnim elementima, cinkom i magnezijem, na osnovi dostupnih literaturnih podataka, kao i naših ispitivanja koja upućuju na to da povećani unos navedenih esencijalnih elemenata pokazuje pozitivne efekte pri ekspoziciji kadmiju. Obrazložena je prednost suplementacije magnezijem pred suplementacijom cinkom i razmatrana preventivna uloga magnezija pri intoksikaciji kadmijem. Ovi su rezultati doprinos rješavanju problema profi lakse i terapije trovanja kadmijem

Convolutional neural network based chart image classification

Charts are frequently embedded objects in digital documents and are used to convey a clear analysis of research results or commercial data trends. These charts are created through different means and may be represented by a variety of patterns such as column charts, line charts and pie charts. Chart recognition is as important as text recognition to automatically comprehend the knowledge within digital document. Chart recognition consists on identifying the chart type and decoding its visual contents into computer understandable values. Previous work in chart image identification has relied on hand crafted features which often fails when dealing with a large amount of data that could contain significant varieties and less common char types. Hence, as a first step towards this goal, in this paper we propose to use a deep learning-based approach that automates the feature extraction step. We present an improved version of the LeNet [LeCu 89] convolutional neural network architecture for chart image classification. We derive 11 classes of visualization (Scatter Plot, Column Chart, etc.) which we use to annotate 3377 chart images. Results show the efficiency of our proposed method with 89.5 % of accuracy rate

Durability of self-compacting rubberized concrete exposed to external sulphate attack

This study evaluated the performance of self-compacting rubberized concrete against external sulphate attack (ESA). cylinders 100 mm in diameter and 220 mm in length of control concrete (no rubber) and rubberized concrete were prepared and tested by visual inspection to identify visible degradation, length and mass variations of specimens, compressive strength, water-accessible porosity, mercury intrusion porosimetry (MIP), and thermal decomposition obtained from thermogravimetric analysis (TGA). Results show that the incorporation of up to 15% rubber enhances the performance against ESA. Rubber reduced the expansion strains and compressive strength. All immersed specimens did not have visible cracks around them. Water porosity was found to increase or decrease versus the time of sulphate immersion. As a result, the impact of rubber on porosity variation is significantly less than the effect of the sulphate-hydrate reaction. Thermogravimetric analysis showed a decrease in portlandite, which is not related to rubber incorporation