ABSTRACT
Agriculture is one of the few remaining sectors that is yet to receive proper attention from the machine learning community. The importance of datasets in the machine learning discipline cannot be overemphasized. The lack of standard and publicly available datasets related to agriculture impedes practitioners of this discipline to harness the full benefit of these powerful computational predictive tools and techniques. To improve this scenario, we develop, to the best of our knowledge, the first-ever standard, ready-to-use, and publicly available dataset of mango leaves. The images are collected from four mango orchards of Bangladesh, one of the top mango-growing countries of the world. The dataset contains 4000 images of about 1800 distinct leaves covering seven diseases. Although the dataset is developed using mango leaves of Bangladesh only, since we deal with diseases that are common across many countries, this dataset is likely to be applicable to identify mango diseases in other countries as well, thereby boosting mango yield. This dataset is expected to draw wide attention from machine learning researchers and practitioners in the field of automated agriculture.
ABSTRACT
HYPOTHESIS: Emerging contaminants (ECs) can interact with soft solid/aqueous interfaces of particulate organic matter and microplastics in the aquatic environment but to what extent? It is hypothesized that EC adsorption can be detected using quartz crystal microbalance (QCM), a sensitive gravimetric tool, and their adsorption energetics and uptake capacity can be measured for various substrates of distinct functional group. This in turn reveals the specific vs. nonspecific interactions. EXPERIMENTS: QCM has been used to detect and measure the adsorption of selected pharmaceuticals, amlodipine (AMP) and carbamazepine (CBZ), onto butyl, carboxyl, amine, and phenyl functionalized self-assembled monolayers (SAMs), mapping out the hydrophobic effect, H-bonding capability, and π- interactions. Adsorption free energy (ΔGads) and maximum interfacial concentration (cmax) for these surfaces are compared. Solvatochromic studies to elucidate the likelihood of H-bonding interactions for CBZ and AMP have been conducted using UV-Vis absorption spectroscopy. FINDINGS: Amlodipine and carbamazepine adsorb onto butyl/aqueous interface with respective ΔGads values of -35.8 ± 1.1 and -37.7 ± 0.1 kJ/mol. Nonspecific interaction allows a greater extent of cmax on the hydrophobic/aqueous interface. CBZ does not bind to the phenyl surface. AMP and CBZ exhibit H-bonding and show proclivity for the amine and carboxyl SAMs. Interfacial chemical environment and adsorbate structural properties play a significant role on EC adsorption.
