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 (2026)
30 Jun 2026 (Vol - 57 , Issue- 07 )
31 Jul 2026 (Vol - 57 , Issue 07 )
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
In vitro evaluation of anthelmintic potential of aqueous and ethanolic leaf extracts of Azadirachta indica and Vitex negundo in benzimidazole-resistant Haemonchus contortus of sheep
This study aimed to evaluate the anthelmintic potential of aqueous and ethanoic leaf extracts of Azadirachta indica and Vitex negundo against benzimidazole-resistant Haemonchus contortus of sheep. A total of 560 dung samples collected from organized sheep farms in Salem, Karur, Kanniyakumari, Kancheepuram and Thiruvallur districts of Tamil Nadu were examined by FECRT to assess the development of resistance to benzimidazole (BZ). The samples from Kancheepuram and Thiruvallur districts showed resistance to benzimidazole by FECRT. The allele-specific PCR (AS-PCR) revealed amplification of a 250bp fragment which confirmed resistance to benzimidazole in samples from Kancheepuram and Thiruvallur districts. Aqueous leaf extracts (ALE) and Ethanolic leaf extracts (ELE) of A. indica and V. negundo at different concentrations viz., 5, 10, 20 and 50 mg/mL, were tested against resistant H. contortus of sheep reared in these farms with BZ resistance. The maximum of 35.42±1.87% inhibition of egg hatch was observed in aqueous leaf extracts (ALE) of A. indica with 50 mg/mL concentration compared to 26.67±1.23 % inhibition of egg hatch in ethanolic leaf extract (ELE). Whereas, the maximum efficacy (%) in egg hatch assay observed in ALE and ELE of V. negundo were 11.67±1.67 and 5.00±0.91, respectively in 50 mg/mL concentration. The maximum mean larval paralysis observed in ALE and ELE of A. indica were 28.89±1.11% and 26.67±1.23 % respectively in 50 mg/mL concentration at 60 min. Whereas, the maximum mean larval paralysis observed in 50 mg/mL concentrations of ALE and ELE of V. negundo were 13.44±1.41 and 7.23±0.55, respectively, at 60 min. It was observed that ALE and ELE of A. indica and V. negundo produced a dose-dependent increase in efficacy in the inhibition of egg hatch and mean larval paralysis. It is concluded that A. indica and V. negundo could be the promising phytomedicines for the alternative control strategy of benzimidazole-resistant nematodes of sheep.
A Review of GLCM, SVM, and MSVM for Early Disease Diagnosis in Sustainable Cotton Farming
This research paper takes a close look at how the grey-level co-occurrence matrix (GLCM), support vector machine (SVM), and multi-class support vector machine (MSVM) methods for texture analysis can be used to find diseases that affect cotton crops. In the field of agriculture, Cotton is vital as the world economy mostly depends on it. Still, the sensitivity of the cotton crop to several diseases seriously jeopardizes productivity and quality. Integration of sophisticated image processing and machine learning techniques has become a potential answer for early and accurate disease identification to handle this problem. The first part of this review emphasizes the need for cotton crops in agriculture and the need for strong disease-detecting techniques. A comprehensive examination of the GLCM approach is presented, focusing on its ability to extract textural features from images. The SVM method is then evaluated for its relevance to disease detection in cotton crops and its efficacy in classification tasks. This paper also discusses the MSVM, a sophisticated variation of the SVM that enables concurrent classification of many categories. Comparative studies are undertaken to assess the advantages and disadvantages of GLCM, SVM, and MSVM for disease detection in cotton crops. The review of significant studies and implementations facilitates comprehension of the beneficial outcomes of these strategies. Moreover, challenges and potential areas for improvement in current methodologies are discussed, setting the stage for future research directions. The overarching objective of this review is to offer a consolidated understanding of state-of-the-art techniques for detecting diseases in cotton crops and to guide researchers, practitioners, and policymakers in implementing effective and scalable solutions for sustainable cotton cultivation.
DEVELOPMENT OF MULTIPLEX PCR FOR SIMULTANEOUS DETECTION OF IMPORTANT TICK-BORNE HEMOPARASITES OF CATTLE
This study aimed to develop a multiplex PCR for simultaneous detection of Theileria annulata, Babesia. bigemina and Anaplasma marginale infections and their carrier status in cattle reared in the different agro-geoclimatic regions of Tamil Nadu, India. The primers for this mPCR were designed from the Cytochrome b gene, 18s rRNA and 16s rRNA for T.annulata, B.bigemina and A.marginale respectively. The specificity of this PCR was 100 per cent for detecting T. annulata and A. marginale and 96 per cent for B.bigemina. The sensitivity of this PCR for detecting the DNA (ng/ml) of T.annulata, B.bigemina and A.marginale was 1.10, 2.09 and 2.59, respectively. Investigations with field samples revealed successful detection of single, double and multiple infections of T.annulata, B.bigemina and A.marginale.
Design, Simulation Analysis, and Experiment on Excavation Device of Self-Propelled Panax Noto-ginseng Combine Harvester
Aiming to address the problems of decreasing the performance rate, high soil resistance, and high damage rate in mechanized harvesting of Panax Notoginseng in hilly and mountainous areas, the self-propelled Panax Notoginseng combine harvester has been designed. The key Working components of the harvester work on separating them from the soil, lifting, and collecting Panax Notoginseng in a single operation. Two shovel types of excavation shovels were tested as the research point to increase performance and decrease the damage rate by selecting the optimum operation parameter via theoretical analysis and mechanical calculation. Two types of excavation shovels (arch and flat) with the three-dimensional model were designed by SolidWorks software and established, then tested and analyzed under three parameters: working speeds of 0.5m/s, 0.7m/s, and 0.9m/s, excavation depths of 140mm, 170mm, and 200mm, and penetration angle of 12◦, 15◦, and 17◦. The results show that the stress, strain, and total deformation of the arch excavation shovel were lower than that of the flat excavation shovel. The EDEM simulation results indicate that the minimal soil resistance of the arch and flat shovels were 163 N and 550 N, respectively. The field experiment results showed that the working speed of 0.7 m/s, the excavation depth of 170 mm, and the penetration angle of 15° were the optimum harvesting parameters that provide a harvest rate of 95.30%, 95.07%, and damage rate of 2.28%, 3.12% for arch and flat shovels respectively.
Economic Analysis of Soyabean Cultivation in Rajasthan State
This study was confined to analyze about the cost of cultivation of soyabean in Jhalawar district of Rajasthan. Soyabean is a valued crop and provides nutritious food for an expanding world population and will become increasingly important with climate change. Soyabean is looked upon not merely as means to supply food for humans and animals, but it also improves the soil fertility by fixing atmospheric nitrogen. Finally, 100 soyabean cultivators from selected villages were selected randomly in proportion to their total number in each size farm group for detailed study. Primary data were collected for agricultural year of 2022-23 and analyzed through various techniques and tools for drawing relevant conclusion. The study of cost of cultivation revealed that, on an average, total cost of cultivation of soyabean was ₹ 37613.83 per hectare. It was highest (₹ 38243.08 per hectare) on small farm and lowest (₹ 36865.49 per hectare) on large farm. Overall gross returns, net returns, farm business income and farm labour income were ₹55363.44, ₹ 17749.60, ₹33518.82 and ₹20242.15 per hectare. Cost of production was highest (₹ 3436.04 per quintal) for small farms and lowest for large farms (₹ 2965.85 per quintal) with an overall average of ₹3186.67 per quintal. On an average, output-input ratio was ₹147.19. For beneficial production of soyabean, it suggested that sufficient labour management in the field should be there. The cultivators should encourage giving an adequate place to soyabean in the cropping pattern at their farm through stakeholder like, Progressive Farmers.