AMA, Agricultural Mechanization in Asia, Africa and Latin America (AMA) (issn: 00845841) is a peer reviewed journal first published online after indexing scopus in 1982. AMA is published by Farm Machinery Industrial Research Corp and Shin-Norinsha Co. AMA publishes every subjects of general engineering and agricultural engineering. Shenyang Jianzhu Daxue Xuebao (Ziran Kexue Ban)/Journal of Shenyang Jianzhu University (Natural Science) General Medicine (ISSN:1311-1817) Chinese Journal of Evidence-Based Medicine Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation Lizi Jiaohuan Yu Xifu/Ion Exchange and Adsorption
AMA, Agricultural Mechanization in Asia, Africa and Latin America (ISSN: 00845841) is a peer-reviewed journal. The journal covers Agricultural and Biological Sciences and all sort of engineering topic. the journal's scopes are in the following fields but not limited to:
Combination mode and working parameters of the cleaning fans of the combine harvester have an important influence on the loss rate and the cleaning rate for different cleaning materials. In this paper, when centrifugal fan was acting alone and cross-flow fan acting alone, the airflow velocity at the indoor space point of cleaning room were measured on a cleaning device. The isokinetic distribution diagrams in different sections were drawn and the characteristics were analyzed. The results showed that under the action of the centrifugal fan, the airflow speed increases as the speed increases. The influence of the cross-flow fan on the screen surface is mainly concentrated on the screen tail, the increase in the speed of the cross-flow fan will expand the scope of influence.
Integration of soil application, seed treatment and foliar spray with fungicide i.e. propiconazole provided protection of spot blotch and increased crop growth and yield of wheat. Among the treatment, the maximum germination with 99.65 % and vigour index with 795.20 was recorded in T3 treatment as seed treatment with bioformulation of T. viride @ 4 % + soil application with vermicompost (1:4) + foliar spray of propiconazole @ 0.1 %). The minimum disease area and disease severity was also recorded in T3 (Seed treatment with bioformulation of T. viride @ 4 % + soil application with vermicompost (1:4) + foliar spray of propiconazole @ 0.1%), representing value as 0.46 cm2 and 10.72 % respectively which was followed by T6 (Seed treatment with bioformulation of T. viride @ 4 % + soil application with mushroom compost (1:4) + foliar spray of propiconazole @ 0.1 %) as 0.52 cm2 and 12.65 %, respectively. The growth promoting effect of wheat crop have also been perceived from integrated approaches. The maximum with 40.15 and 27.40 cm shoot and root length were found in T3 treatment (Seed treatment with bioformulation of T. viride @ 4 % + soil application with vermicompost (1:4) + foliar spray of propiconazole @ 0.1 %). The highest grain yield with 39.01g was also obtained in T3 (Seed treatment with bioformulation of T. viride @ 4 % + soil application with vermicompost (1:4) + foliar spray of propiconazole @ 0.1 %), representing 80.43 per cent increased over control.
Induced resistance using inorganic chemical have ability to reduce the disease incidence of Fusarium wilt in tomato from 78.50 to 9.12 per cent in 2015, 88.50 to 11.00 per cent in 2016 and 90.96 to 9.30 per cent in 2017 at 15 days after inoculation with the minimum calcium chloride treated plants. The tomato plant treated with inorganic chemical as inducers sensitized to produce increased level of soluble protein and total phenol contents with the maximum in calcium chloride treated tomato leaves indicating 34.83, 35.25 and 34.40mg/g in 2015, 35.93, 36.27 and 35.22 mg/gm in 2016 and 35.06, 35.96 and 33.20 mg/g of fresh leaves in 2017 at 5, 10 and 15 days of pathogen inoculation. Similarly, total phenol content was also found maximum in calcium chloride treated plant. Correlation coefficient analysis revealed that there was negative correlation between disease incidence with soluble protein (r = -0.548, -0.564 and-0.519 in 2015, -0.571, -0.570 and -0.517 in 2016 and -0.0.640, -0.643 and -0.635 in 2017) and total phenol (r = -0.576, -0.562 and -0.580 in 2015, -0.528, -0.564 and -0.536 in 2016 and -0.634, -0.521 and -0.536 in 2017) content at 5, 10 and 15 days of treatment.
Agriculture is the backbone for sustainability of any country and plants has a vital role to play in biodiversity sustenance. Crop yield is highly correlated with plant health. Early detection of diseased plant can reduce the adverse effect on healthy plant. Plant leaf is the primary component to identify the abnormality in a plant. Plant leaf images captured by advanced digital cameras can be passed to an advanced computer added system for automated detection of diseased plant at very early stage for fast response. Performing the same process by human being for individual plant is an inefficient and time consuming process which may lead to spreading the disease in whole crop field. Availability of high resolution and GPS enabled digital cameras and advancement in image processing techniques can be utilized to overcome this challenge of early detection of plant disease through plant leaf. The features extracted from image processing tool will be passed a pre-trained deep learning Convolutional Neural Network (CNN) based models for recognizing and classifying the plant disease. Transfer Learning approach is used to increase the efficiency and accuracy of the proposed system. Data augmentation and data balancing techniques are also employed to overcome the overfitting issue. Additionally, the performance of transfer learning approach has been improved in significant manner after adopting efficient pooling and optimization technique. Total 17820 images of different plant leaves (healthy and un-healthy) are used to train and validate pre-trained CNN models. ROC (Receiver Operating Characteristic) curve and other statistical parameters, including specificity, sensitivity, recall, precision and accuracy was applied to compare the performance of various pre-trained model used in transfer learning. The results are clearly indicates that AUC (Area Under Curve) values for implemented models are high and approaching to 0.942. Inclusion of efficient pooling strategy and optimization technique has increased the accuracy by 4-5%. Initially the was ranging from 92% to 95% but after adapting pooling and optimizer the accuracy enhanced to 95%-98%.
The combined influence of conventional agricultural practice and booming population, entice the advanced research on sustainable agriculture. The basic goal of sustainable agriculture includes environmental health, social & economic equity and economic viability, which can be achieved by supplementation of plant probiotics. It will not only fulfil the prime goals of sustainable agriculture but also enhance microbial biodiversity in soil. These latent microbes when applied to host plants, colonize independently or as endophytes and are potentially involved in plant growth promotion, nitrogen fixation, siderophore production, phosphate solubilization and biocontrol activities. Additionally, they have a unique property to break down the complex nutrients into simpler ones thereby improving the soil fertility. The phytobiomes act as an eco-friendly substitute for chemical fertilizers as it promotes plant health, growth & productivity along with soil health leading to organic farming. Hence, this review put forth views pertaining to the traits and applications of potent plant probiotics for sustainable agriculture.