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
COMPUTATIONAL IMAGE PROCESSING FOR DEFECT DETECTION AND QUALITY ASSESSMENT OF AGRICULTURAL PRODUCTS
This study investigates the application of computational image processing techniques for detecting mechanical damage and quality assessment in agricultural products, with a focus on Golden Delicious apples. Post-harvest fruits are exposed to physical and chemical stresses that alter their surface properties, which can be quantitatively analyzed using computer vision. By employing MATLAB-based algorithms in the HSV (Hue, Saturation, Value) color space, color variations were segmented and morphological operations were applied to identify defect regions. A controlled imaging system was designed to minimize shadow effects from multiple light sources, ensuring reproducibility of results. The percentage of damaged areas was calculated, providing an objective metric for quality evaluation. Beyond agricultural applications, this computational approach demonstrates the potential of image-based defect quantification as a material characterization method, aligning with the broader scope of computational materials science. The integration of image segmentation, morphological analysis, and quantitative defect evaluation highlights the role of computer vision as a scalable and reliable tool for material quality assessment.
Effect of Residual Weed Biomass and Nitrogen Levels on Growth and physiology of Toria in Rice-Based Cropping System
A field experiment was conducted to evaluate the effect of residual weed biomass incorporation and nitrogen levels on growth and physiological parameters of toria in a rice-based cropping system. The experiment consisted of two weed biomass treatments (with incorporation, W₁; without incorporation, W₀) and varying nitrogen levels (N₀ to N₁₆₀). Results revealed that plant height, dry matter accumulation, and leaf area index (LAI) were significantly influenced by both weed biomass incorporation and nitrogen levels at all growth stages, whereas their interaction (W × N) remained non-significant. Incorporation of weed biomass (W₁) consistently recorded higher growth attributes compared to W₀. Similarly, increasing nitrogen levels significantly enhanced these parameters, with maximum values observed under N₁₆₀. Physiological parameters, including Crop Growth Rate (CGR) and Relative Growth Rate (RGR), were also significantly improved with weed biomass incorporation and higher nitrogen levels, while their interaction effect remained non-significant. The highest CGR and RGR were recorded under W₁ and N₁₆₀ treatments. In contrast, Net Assimilation Rate (NAR) was not significantly affected by weed biomass incorporation but showed significant variation with nitrogen levels. NAR increased with higher nitrogen at early growth stages but declined at later stages due to increased canopy development and mutual shading effects. Overall, the study indicated that incorporation of weed biomass and higher nitrogen application improved growth and physiological efficiency of toria independently. The results suggest that integrating organic biomass with optimal nitrogen fertilization enhances crop performance by improving soil conditions, nutrient availability, and photosynthetic capacity in rice-based cropping systems.
“Integrative approaches to multiple-stress resistance in cereals: mechanisms, genomics and breeding strategies: A Review”
Cereal crops such as rice, wheat, maize, sorghum, barley, and millets are staple food sources for more than half of the global population. These crops are frequently exposed to multiple stresses simultaneously or sequentially, including drought, heat, salinity, flooding, and diverse biotic factors such as pathogens and insect pests. Combined stresses typically cause greater yield losses than individual stresses, due to synergistic and often unpredictable physiological and molecular responses. Recent research highlights that plants under multiple stresses exhibit distinct morpho-physiological alterations such as accelerated senescence, impaired photosynthesis, reduced reproductive success, and hydraulic dysfunction. Biochemically, stress-induced accumulation of reactive oxygen species (ROS) is more pronounced under combined stress conditions, requiring enhanced antioxidant responses, osmolyte biosynthesis, and metabolic reprogramming. Hormonal crosstalk, especially among ABA, JA, SA, and ethylene, emerges as a central integrator of stress signals. At the molecular level, transcription factors such as DREB, NAC, and WRKY families orchestrate transcriptional reprogramming, while QTLs and candidate genes for multi-stress tolerance are increasingly being identified through genomics and phenomics approaches. Despite these advances, breeding for multi-stress resistance remains a formidable challenge due to genotype × environment interactions, trait complexity, and yield penalties under favourable conditions. Integrated breeding strategies, leveraging genomic selection, CRISPR-based editing, high-throughput phenotyping, and agronomic management, are essential to achieve climate-resilient cereals for future food security.
Molecular Assessment of Genetic Diversity and Identification of Drought-Tolerant Indigenous Rice (Oryza sativa L.) Cultivar of Manipur using SSR Markers
Rice (Oryza sativa L.) is a major staple crop supporting the livelihood and food security of more than half of the world’s population. However, at present drought stress is one of the most critical abiotic constraints affecting rice productivity, particularly in rainfed ecosystems. The present study was undertaken to assess the genetic diversity of 30 rice genotypes, including 23 indigenous cultivars from Manipur and 7 released varieties, were evaluated using 17 SSR markers associated with drought tolerance Quantitative Trait Loci (QTLs). Genomic DNA was extracted using the CTAB method and amplified through PCR. Out of the 17 markers used, 15 showed polymorphism and were used for further analysis. The polymorphic information content (PIC) values ranged from 0.50 to 0.78 with an average of 0.64, indicating that the selected markers were highly informative for diversity analysis. Genetic dissimilarity among the genotypes was estimated using Jaccard’s dissimilarity coefficient, which ranged from 0.19 to 0.98, demonstrating considerable genetic variation among the studied genotypes. The minimum dissimilarity was observed between Tomila and Hungyo, whereas the maximum dissimilarity was recorded between Shbhagi dhan and RCPL-17-1. Cluster analysis using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) grouped the 30 genotypes into four distinct clusters. Cluster I contained 8 genotypes including the drought-tolerant check Sahbhagi Dhan and MAS-26, while Cluster II, III, and IV comprised 7, 9, and 6 genotypes respectively. The results indicate that SSR markers linked QTLs associated with drought tolerance are effective tools for assessing genetic diversity in rice. The identified diverse genotypes can serve as valuable genetic resources for marker-assisted breeding programs aimed at developing drought-tolerant rice varieties for the rainfed ecosystems.
Biological and Genetic Resources for the Eco-Friendly Management of Major Mustard Diseases – A Comprehensive Review
Indian mustard (Brassica juncea L. Czern. & Coss.) is a major oilseed crop whose productivity is severely constrained by fungal, bacterial, and viral diseases, notably Alternaria blight, Sclerotinia stem rot, white rust, and Turnip mosaic virus, causing yield losses of up to 60% under favourable conditions. Dependence on chemical pesticides for disease control has resulted in environmental contamination, resistance development, and residue-related concerns, underscoring the need for sustainable alternatives. Biological control using beneficial microorganisms such as Trichoderma, Bacillus, Pseudomonas, and Streptomyces has shown considerable potential in suppressing mustard pathogens through competition, antibiosis, mycoparasitism, lytic enzyme production, and induction of systemic resistance. Field-level application of these agents via seed treatment, soil application, root dipping, and foliar sprays has consistently reduced disease severity and improved crop performance. In parallel, host plant resistance provides a durable management option, with resistant or tolerant genotypes including Pusa Bold, RH-749, NRCDR-2, RH-406, and Giriraj, and resistance genes such as Rcr1, LepR1–LepR3, and BjuWRR1 contributing to enhanced disease resilience. Integrating resistant germplasm with compatible microbial biocontrol agents offers synergistic and stable disease suppression, reduces reliance on chemical fungicides, and supports climate-resilient, eco-friendly mustard production across diverse agro-ecological regions.