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. Azerbaijan Medical Journal Gongcheng Kexue Yu Jishu/Advanced Engineering Science Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Interventional Pulmonology Kongzhi yu Juece/Control and Decision Zhenkong Kexue yu Jishu Xuebao/Journal of Vacuum Science and Technology Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) Zhonghua yi shi za zhi (Beijing, China : 1980)
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:
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.
Soil legacy phosphorus (P) accumulated due to the long-term continuous application of phosphatic fertilizers in agricultural fields retained in the soil for years, as they are sparingly soluble. This can be made as a possible soil P source using phosphorus activators. In this study, a laboratory incubation and a pot experiment was conducted to evaluate the potential of some P activators (Phosphorus Solubilising Bacteria, Phytase, Humic acid, Oxalic acid, Farmyard Manure) in increasing the availability of residual phosphorus in calcareous soil. For incubation experiment, the P activators were applied alone and combined. Soil samples were analyzed at subsequent intervals for Olsen P, alkaline phosphatase activity, and sequential P fractions. The results showed that Farmyard Manure (FYM) application with Humic acid (HA) increased the available soil phosphorus and alkaline phosphatase activity. All treatments have a significant effect on different soil P-fractions. A greater reduction in calcium phosphate fraction was noted in FYM and Humic acid application. The best five treatments selected from the laboratory incubated experiment were used for the pot experiment with maize, along with the different dosages of P fertilizer. The morphological parameters were observed on 30th and 60th days of the maize crop. The results show that the application of FYM and Humic acid with 100% recommended dose of P was statistically comparable with FYM and Humic acid with 75% recommended dose of P. The findings have illustrated the potential mechanisms of P release by different P activators and the efficiency of P activators in increasing the legacy P availability.
Ninety mungbean [Vigna radiata (L.) Wilczek] germplasm accessions were evaluated for resistance against leaf spot disease caused by Cercospora canescens under field conditions during rabi 2018 and rabi 2019. A disease rating scale of 1-5 was used for the evaluation of resistance of mungbean accessions. A considerable variation was found among the genotypes with respect to the disease reaction. Thirty-two accessions; were found resistant and thirty-five accessions were found to have moderately resistant reactions against the CLS disease. The rest of the accessions were either susceptible or moderately susceptible or highly susceptible. On the basis of present investigations, resistant and moderately resistant genotypes identified against CLS disease may be exploited in the breeding program aimed at the development of a high-level resistant variety of mungbean against Cercospora leaf spot.