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.
WOS Indexed (2025)
07 Dec 2025 (Vol - 56 , Issue- 12 )
31 Dec 2025 (Vol - 56 , Issue 12 )
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:
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
Complexity of Subsystems interaction on Watershed Hydrological Retort. A case study of Ikpoba River.
Hydrological reaction of watershed to changing climate is among main features of hydrology. To x-ray the impact of climate transformation on the hydrological reaction of Ikpoba watershed, the watershed was first demarcated so as to delineate the basins and the corresponding sub-basins within the watershed. Result shows that the watershed has a total soil area of 5078.2509ha, which categorize the landuse definition into forest mixed with total area of 1452.0287ha, agriculture (total area of 1835.3243ha), residential (total area of 1655.1857 and water (total area of 135.7022). The region occupied by water was witnessed to be very insignificant which might not be the true illustration of what is on ground, but this is correct in model development since the orthophoric (areal) assessment of the watershed shown that the entire water body within the basin is covered by water hyacinth which was why the model appraised the total region occupied by the water body as 135.7022ha equated to forest mixed which was projected as 1452.0287ha.
The Effect of different fertility levels and zinc fertilization on Growth and Yield of baby corn (Zea mays L.) hybrid under Southern Rajasthan
A field experiment was conducted during kharif season of 2019-20 and 2020-21 at the Rajasthan College of Agriculture, Maharana Pratap University of Agricultural & Technology (MPUAT), Udaipur. The objective is evaluating the performance of genotypes under varying fertility levels. The treatments consisted of three factors viz. baby corn hybrids [HM-4, Pratap hybrid maize-3 (PHM-3)], NPK levels [(i)125:60:50 kg N, P2O5 and K2O ha-1 (ii) 150:70:60 kg N, P2O5 and K2O ha-1 (iii) 175:80:70 kg N, P2O5 and K2O ha-1] and zinc fertilization [(i) 5 kg Zn ha-1 (ii) 3.75 Kg Zn ha-1+ ZnSB (iii) 2.50 Kg Zn ha-1+ ZnSB]. The experiment was laid out in factorial randomized block design with three replications. The results revealed that plant growth, dry matter accumulation, cob yield of baby corn and green fodder yield varying significantly with fertility levels and genotypes. Plant height, dry matter accumulation, cob yield and green fodder yield recorded higher with higher nutrient levels compared lower nutrient level. The maximum cob yield (8012.15 kg/ha), green fodder yield (26444.26 kg/ha), obtained with HM-4 which was significantly higher than Pratap hybrid maize-3 (PHM-3).
Root parameters, quality parameter, yield, and nutrient content of Rabi fennel (Foeniculum vulgare Mill.) as influenced by different drip irrigation levels, crop geometry and mulching
The experiment was carried out on root parameters, quality parameter, yield and nutrient content in fennel (Foeniculum vulgare Mill.) as influenced by different drip irrigation levels, crop geometry and mulching. The experiment was laid out in a split-split plot design with comprised of four irrigation levels in the main plot, three crop geometry in the sub-plot and two mulch treatments in the sub-sub plot and replicated thrice. The results revealed that the highest fresh weight of root (8.16 g plant-1), dry weight of root (2.98 g plant-1), root volume (8.44 cubic cm plant-1), and root: shoot ratio (0.123) of fennel at 100 DAS were recorded with drip irrigation level of 0.8 ETc. Further, analysis of data indicated that higher values of quality parameters, nutrient content and their uptake by fennel were recorded under irrigation level of 1.0 ETc. However, 0.8 and 1.0 ETc gave at par values of all these parameters. Further data indicated that paired row sowing at 40 x 60 cm spacing gave the highest fresh weight of root, root dry weight, root volume, yield. In mulch, plastic mulch treatment recorded maximum fresh weight of root, root dry weight, yield, quality parameters and nutrient content and uptake in fennel seed. However, the maximum yield of 1816 kg ha-1 was obtained when fennel crop was sown in paired row sowing of 40 x 60 cm with plastic mulch along with water supply of 80% of crop water requirement (ETc) through drip system.
Soil biological properties and yield of sweet corn (Zea mays L. saccharata) as influenced by different sources of nitrogen in typic haplusteps
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
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.