In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.

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Zinc oxide (ZnO) and magnesium-doped zinc oxide nanoparticles, Zn 0.88 Mg 0.12 O (ZMO), were prepared by autocombustion method. Further, nanocomposites of the asprepared nanoparticles with microwave-synthesized reduced graphene oxide (rGO) nanosheets, ZnO−rGO and ZMO− rGO, have also been prepared with a view to see the effect of doping of magnesium in zinc oxide on the tribological properties of the nanocomposite. Morphologies of nanoparticles/nanosheets and their nanohybrids have been studied by employing scanning electron microscopy (SEM)/highresolution (HR) SEM with energy-dispersive X-ray (EDX), transmission electron microscopy (TEM)/HR-TEM, X-ray diffraction, Fourier transform infrared, UV−visible, Raman, and Xray photoelectron spectroscopy (XPS) techniques. Triboactivity of the additives in paraffin oil has been interpreted considering the parameters mean wear scar diameter, coefficient of friction, load-carrying capacity, and wear rates obtained from ASTM D4172 and ASTM D5183 tests using a four-ball lubricant tester at optimized concentration (0.125% w/v). The performance of base lube and its admixtures has been found to lie in the order ZMO−rGO > ZnO−rGO > ZMO > ZnO > rGO > paraffin oil. Outstanding enhancement in triboactivity of nanocomposites, particularly that of ZMO−rGO indicates that nanoparticles are irrefutably instrumental in reinforcement of rGO, and on the other hand, rGO is associated with abatement of agglomeration of the nanoparticles. Thus, interactions between rGO and nanoparticles are vehemently synergic in nature. It is noteworthy that the best results were obtained with the following optimized concentrations: ZnO/ZMO 0.25%; rGO 0.15% and composites 0.125% w/v. Morphological studies of the wear track lubricated with different additives have been performed using SEM and contact mode atomic force microscopy. Results are in conformity with the order given above. The EDX analysis of ZMO−rGO exhibits the presence of zinc and magnesium on the worn surface, supporting their role in the formation of in situ tribofilm. Their role is further corroborated by XPS studies. Owing to their excellent tribological behavior, these sulfur-and phosphorusfree composites may be recommended as potential wear and friction modifiers.

Summary We are facing an onslaught of chronic and recurrent dermatophytosis in epidemic proportions never encountered previously. There is a dearth of studies assessing the quality of life (QoL) and psychological morbidity in patients with superficial dermatophytosis. Our aim was to assess QoL and psychological morbidity in a sample of Indian patients suffering from dermatophytosis by using Dermatology Life Quality Index (DLQI) questionnaire and General Health Questionnaire (GHQ), respectively. This was a single‐centre, cross‐sectional study where consecutive patients of first episode, chronic or recurrent dermatophytosis were invited to participate. In addition to DLQI and GHQ12, patients' demographic data, duration and symptoms of dermatophyte infection, were also documented and recorded in the case record form. We recruited 196 patients who satisfied the inclusion criteria. The mean total DLQI score was 13.41 ± 7.56 (range 0‐30). The main items in the questionnaire influenced by the disease were “symptoms and feelings,” followed by “daily activities,” “leisure” and “personal relationships.” Age of the patient and body surface area involved had a significant impact on the QoL in our study (P ≤ 0.05). The mean GHQ‐12 score was 16.98; 84.9% of patients had a score higher than or equal to 12 indicating significant psychological distress. GHQ‐12 was found to have significant correlation with the DLQI score. Quality of life issues and psychosocial aspect should be considered while managing dermatophytosis as education about the disease, its management and prognosis may go a long way in improving the adherence to treatment and overall outcome in these patients.