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)
07 May 2026 (Vol - 57 , Issue- 05 )
31 May 2026 (Vol - 57 , Issue 05 )
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
Elevated CO₂ and Temperature Alter Leaf Functional Traits and Growth Efficiency of Groundnut (Arachis hypogaea L.): Role of Herbicide Dosage and Crop-Weed Photosynthetic Type
A pot culture experiment was conducted in a Carbon dioxide and Temperature Gradient Chamber (CTGC) at ICAR-CRIDA, Hyderabad, during two consecutive kharif seasons (2022-23 and 2023-24) to study the influence of elevated temperature (eT), elevated CO₂ (eCO₂), herbicide dosages and crop-weed interactions on Specific Leaf Area (SLA), Relative Growth Rate (RGR) and Net Assimilation Rate (NAR) of groundnut (Arachis hypogaea L.) cv. K-6. Elevated temperature (eT) recorded the highest SLA (289.5 cm²/g) but the lowest RGR (0.415 g/g/day), indicating heat-induced leaf expansion concurrent with growth limitation. Elevated CO₂ (eCO₂) alone yielded the lowest SLA (217.8 cm²/g) but a higher RGR (0.468 g/g/day), reflecting more efficient dry matter accumulation per unit leaf area. The combined eT+eCO₂ treatment registered the highest RGR (0.474 g/g/day), confirming that elevated CO₂ partially ameliorated heat-induced growth limitation. Net Assimilation Rate was highest under ambient conditions (9.98 g/m²/day), declining progressively under all elevated treatments. The 1.5X dose of Imazethapyr + Propaquizafop recorded the most favourable RGR (0.483 g/g/day) and late-season NAR (2.01 g/m²/day) without significantly influencing SLA. Among crop-weed combinations, G+C4 recorded the highest SLA (274.8 cm²/g), while G+C3 & C4 exhibited the highest NAR (8.94 g/m²/day). C4 weed species were less competitive than C3 weeds for growth resources, thereby imposing lesser constraint on groundnut growth and carbon assimilation.
Evaluation of Azadirachta indica as a Plant-Based antimicrobial agent against Staphylococcus aureus
Staphylococcus aureus is an important Gram-positive pathogen responsible for a wide range of infections in humans and animals, including mastitis, skin infections, and systemic diseases. The increasing resistance of S. aureus to conventional antibiotics has encouraged the exploration of plant-based antimicrobial agents as potential alternatives. The present study evaluated the In-vitro antibacterial efficacy of aqueous leaf extract of Azadirachta indica against Staphylococcus aureus isolates obtained from small ruminants and their human handlers. Antibacterial activity of the neem extract was assessed using the agar well diffusion method, along with determination of the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). The neem extract demonstrated concentration-dependent antibacterial activity against S. aureus isolates, producing a maximum zone of inhibition of 15.5 mm at higher concentrations. Both MIC and MBC were recorded at 125 mg/mL, indicating a bactericidal effect of the extract. The findings suggest that Azadirachta indica possesses significant antibacterial activity against Staphylococcus aureus and may serve as a promising herbal alternative to conventional antibiotics. Further studies are required to isolate active phytochemicals and evaluate their therapeutic applicability.
Factors Affecting the Sugarcane Yield and Sucrose Accumulation Influencing Efficient Biofuel Production
The persistent reliance on fossil fuels, contributing significantly to global energy consumption and environmental degradation, necessitates alternative renewable energy sources such as biofuels. Sugarcane, a C4 crop with high biomass and sugar content, serves as a prime feedstock for first- and second-generation bioethanol production. This study evaluated ten diverse sugarcane genotypes at the Punjab Agricultural University Regional Research Station, Kapurthala, during 2022–23 to assess morphological and biochemical traits, including stalk length, sugar content, and fibre to infer bioethanol production potential of genotypes. Statistical analyses revealed genotype-dependent variability, underscoring the need for selecting high-yield, cost-effective carbohydrate sources for sustainable bioethanol production. Genotype CoPb18213 demonstrated superior performance in biomass yield and juice quality, making it a promising candidate for integrated first- and second-generation biofuel production. The findings underscore the importance of integrating high-yielding genotypes with efficient crop management and mechanization practices to enhance biomass utilization, reduce post-harvest losses, and improve overall biofuel production efficiency. This study provides valuable insights for the development of sugarcane varieties optimized for sustainable and economically viable bioenergy production.
EVOLUTION OF TIME SERIES MODELS FOR AGRICULTURAL FORECASTING: A REVIEW OF STATISTICAL, MACHINE LEARNING, AND DEEP LEARNING APPROACHES
Accurate forecasting of crop yields plays a pivotal role in ensuring food security, guiding policy decisions, and optimizing resource management. This review brings together more than fifty years of progress in time series forecasting for agriculture, tracing the evolution from classical statistical approaches (1970s–1990s) to advanced time series models (1990s–2010s), and most recently to deep learning architectures (2015–2025). The methods examined include multiple regression, principal component analysis (PCA), logistic regression, autoregressive integrated moving average (ARIMA) models, state space formulations, and a growing array of machine learning techniques such as long short-term memory (LSTM) networks, convolutional neural networks (CNN), and Transformer-based models. Through a structured comparative lens, this review assesses the strengths, limitations, data requirements, and computational demands of each methodological category. The findings underscore that no single approach is universally optimal; the choice of model depends on factors such as data availability, forecast horizon, computational capacity, and the specific agricultural context. Recent advances highlight the promise of hybrid models that integrate complementary techniques, offering improved predictive accuracy while preserving interpretability. A central challenge identified is climate non-stationarity, which calls for adaptive forecasting methods. At the same time, the convergence of advanced analytics, satellite remote sensing, IoT sensor networks, and climate science is opening unprecedented opportunities for agricultural prediction. Looking ahead, future research directions include the development of climate-adaptive forecasting systems, hybrid frameworks that combine mechanistic and learning-based approaches, and explainable artificial intelligence tailored to agricultural applications.
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.