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AMA, Agricultural Mechanization in Asia, Africa and Latin America

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.

Submission Deadline
07 Dec 2022 (Vol - 53 , Issue- 12 )
Upcoming Publication
30 Nov 2022 (Vol - 53 , Issue 11 )

Aim and Scope :

AMA, Agricultural Mechanization in Asia, Africa and Latin America

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:

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
Agricultural and Biological Sciences
Electrical Engineering and Telecommunication
Electronic Engineering
Computer Science & Engineering
Civil and architectural engineering
Mechanical and Materials Engineering
Transportation Engineering
Industrial Engineering
Industrial and Commercial Design
Information Engineering
Chemical Engineering
Food Engineering

Soil biological properties and yield of sweet corn (Zea mays L. saccharata) as influenced by different sources of nitrogen in typic haplusteps

Paper ID- AMA-04-02-2022-11090

A field study was carried out to investigate the soil biological properties and yield of sweet corn (Zea mays L. saccharata) as influenced by different sources of nitrogen in typic haplusteps of Rajasthan during kharif, 2019 at the Instructional Farm of the Rajasthan College of Agriculture, Udaipur with twelve treatments i.e. 100% RDN through urea, 75% RDN through urea + 25% N – FYM, 50% RDN through urea + 50% N – FYM, 75% RDN through urea + 25% N – VC, 50% RDN through urea + 50% N – VC, 50% RDN through urea + 25% N – FYM + 25% N – VC, 125% RDN through urea, 100% RDN through urea + 25% N – FYM, 75% RDN through urea + 50% N – FYM, 100% RDN through urea + 25% N – VC, 75% RDN through urea + 50% N – VC, 75% RDN through urea + 25% N – FYM + 25% N – VC. . Experiment was laid out in randomized block design with three replications. Results revealed that application of 125% RDN through urea recorded significantly higher green cob yield, green stover yield and green fodder yield. However, soil biological properties like microbial population and enzymatic activities were highest with application of 75% RDN through urea + 50% N – FYM. The present findings indicated that 125% RDN through urea recorded higher yields, but to sustain the soil health 75% RDN through urea and 50% RDN through FYM have been recommended.

Convolutional Neural Network with Xgboost for Image-Based Thrips Detection on Plantain Trees

Paper ID- AMA-04-02-2022-11089

Pests and diseases are the major concerns in plantain tree cultivation. Thrips are one of the major pests found in banana fields. It is also known as Thysanoptera. These are the tiny insets about 1-3 mm in size and about 5500 species, are the widely seen pests in the plantain trees of India. Their attack is infecting the health and growth of the host plants. The detection of thrips at the early stages is very difficult because of their minute size. Traditional methods such as printed dichotomous taxonomic and genetic markers require laboratory facilities and human expertise. Automated identification of pests is essential for controlling various plant diseases effectively. Deep learning techniques, especially, Convolutional Neural Networks (CNNs) is already established their proficiency in the automatic detection of diseases and pests in humans. This research work proposes a CNN model that combines the efficiency of the Visual Geometric Group (VGG) model with the Xgboost technique for identifying pests. The experiments are carried out on banana plants for detecting the thrips. Banana image dataset is a real-time dataset collected from the state named Tamil Nadu situated in the Southern part of India. The proposed model is compared with the basic VGG-19 model and the result shows the proposed Xgboost-VGG model outperforms in terms of the evaluation metrics: recall, accuracy, f1-score, precision, and specificity.

Effect of Foliar Application of Zinc Based Nanofertilizer and Varying Fertility Levels on Growth Attributes, Yield Attributes, Yield and Economics of Maize

Paper ID- AMA-04-02-2022-11088

A field experiment was conducted during two consecutive Kharif, seasons of 2020 and 2021 at Instructional Farm, Rajasthan College of Agriculture, Udaipur to evaluate the effect of foliar application of zinc based nanofertilizer and different fertility levels on growth attributes, yield attributes, yield and economics of maize. The experiment was laid out in a factorial randomized design with three replications comprising four foliar application of nanofertilizer (Control, at knee high stage, at 50% tasseling stage and both at knee high stage and at 50% tasseling stage) and four fertility levels (100% RDF, 90% RDF, 80% RDF and control). Significantly highest grain, stover and biological yield (51.90, 82.32 and 134.21q ha-1) were recorded with the dual foliar application of nanofertilizer at knee high stage and at 50 per cent tasseling stage over single stage foliar application. Among different levels of fertility, application of 90 per cent RDF significantly increased grain, stover and biological yield. Yield attributing characters viz., cob length (cm), girth of cob (cm), grains cob-1, cob height (cm), grain weight cob-1 (g), 1000 grain weight (g) and Shelling (%) were significantly higher with the dual foliar application of nanofertilizer at knee high stage and at 50 per cent tasseling stage and application of 90 per cent RDF in maize. Similarly, the significantly highest protein content of maize (11.13 % and 10.97 %) was found in with dual foliar application of nanofertilizer and 90 per cent RDF, respectively. The significantly highest net return and B:C ratio were found under dual foliar application of nanofertilizer 82956 and 3.04) and soil application of 90 per cent RDF (`86112 and 3.15) in maize.

Evaluation of Biochemical Basis of Resistance in Mustard against Powdery Mildew

Paper ID- AMA-04-02-2022-11087

Indian mustard [Brassica juncea (L.) Czern & Coss] is an important oilseed crop. Powdery mildew of mustard incited by Erysiphe cruciferarum (Opiz ex Junell) is the most important disease that causes a maximum reduction in yield and quality of mustard seed. The resistance of plants to various pathogens depends on the synthesis and level of various defense enzymes like hydrolyses peroxidases, phenol and PR-Protein. The present study focused on powdery mildew and its biochemical correlation with TSS, PR-Proteins and phenols content. The field studies observed that this disease incidence data were correlated with biochemical changes and levels of TSS, PR-protein and phenols activities. Among biochemical basis of resistance in mustard, TSS and Protein are maximum in healthy plants compared to infected plants, and Phenol content is minimum in healthy plants compared to infected plants were observed in leaves of infected plants with Erysiphe cruciferarum compared to the healthy ones. Biochemical changes in total soluble sugars, protein and phenol content were played a significant role in imparting resistance against this disease. A similar trend in these biochemical was also observed at tender and maturing stages of leaves.

Assessment of Principal Component Analysis and Genetic diversity among linseed (Linum usitatissimum L.) germplasm under Humid South Eastern plain of Rajasthan

Paper ID- AMA-04-02-2022-11086

The present investigation was carried out to assess PCA and genetic diversity in linseed germplasm during Rabi 2019-20 at Agricultural Research Station, Ummedganj, Kota, Rajasthan to determine level of variability among twenty nine rice genotypes in randomised block design using principal component analysis. First three principal components exhibited more than one Eigen values and accounted for 74.02% percent of total variation. PC1 accounted 42.79% of the total variability contributed by the traits like number of capsule per plant, seed per capsule, thousand grain weight and branches per plant whereas PC2 account 17.60% of the total variation that was contributed by the traits viz. number of capsule per plant, seed per capsule, plant stand, plant height and days to 50% flowering. PC3 had the contribution from the characters like plant height and 1000-grain weight. Thus, the results revealed vast genetic variation and the traits contributing for the variation in linseed genotypes which can be used for various breeding programmes for improvement in yield and quality. Cluster I consisted of 11 cultivars showed maximum mean grain yield. Cluster I had genotypes having higher mean values for characters like seed yield, thousand weight and also desirable for early flowering trait. Cluster VI had the highest mean values yield contributing traits like branches per plant, seed per capsule and capsule per pod that had significant positive correlation with grain yield. Cultivars having favourable characteristics from these clusters could be employed as prospective donors in a future hybridization programme to generate high yielders.