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
Callus mutagenesis using EMS and mutagenic response in aromatic rice (Oryza sativa L.) landraces of India
Induced mutagenesis in crop plants has created avenues for improving desirable genetic changes without altering the unique genetic background of the promising cultivars. A study was carried out to find the efficiency and effectiveness of 0.2% Ethyl Methane Sulfonate (EMS) mutagen on four popular but tall local aromatic rice landraces, Kalikati, Basumati, Gangabali and Karpurajeera by treating the calli initiated from their mature embryos for three different durations (2 hours, 4 hours and 6 hours). A reduced shoot regeneration efficiency was witnessed in Kalikati and Basumati (59.54% and 61.10% reduced respectively) while it increased in Gangabali and Karpurajeera (23.32% and 29.76 % increased respectively) with increasing treatment duration, compared to control. Among the four types of chlorophyll mutants observed, albina were most frequent in all the genotypes except Basumati, where virdis mutants followed by albina were highest whereas, in Kalikati, chlorina mutants were followed by albina in high frequency. In general, mutagenic effectiveness and efficiency were reduced with an increase in the duration of treatment in all the genotypes except in Basumati and Karpurajeera where mutagenic efficiency was highest at mid-treatment duration (4 hours). Mutation rate of 10.68 observed in Basumati was the highest among the genotypes indicated high mutagenic effect on the calli of this aromatic rice landrace. Genotypic differences in frequency of mutants, effectiveness and efficiency of the mutagen on the aromatic rice genotypes were clearly evident. This research will be useful in mutation breeding programmes, involving economically important crops, within a limited time and space.
Detection method of phenological distribution of Apple flower based on YOLO-CG
The estimation of crop phenological distribution is of great importance for controlling time of thinning flowers. In order to improve the efficiency of flower thinning in modern orchard, a detection method of apple flower phenological distribution based on YOLO-CG network model is proposed to detect, which aims at improving incomprehensive and low-efficient manual traditional detection method of apple flower phenological distribution. First of all, the YOLO-CG network model is to integrate the CA mechanism into the YOLOv5 network, which could obtain more shallow features to improve network performance; Secondly, in order to improve the training speed to reduce the calculation amount of the network model, the Ghost-Bottleneck module is proposed to replace the Bottleneck module; Finally, the CIOU is used as the bounding box regression loss function to improve the stability of the target box regression. The model is fine-tuned and trained with manually-marked apple flower images in 4 phenological stages. The proposed method was compared with the detection models of YOLOv3, YOLO v4, YOLO v5 and Faster R-CNN, and the detection performance of apple flower under different shooting conditions are discussed, which proves the effectiveness of this method. Experimental results show that the mAP value of apple flower detection at different stages was 94.90%, an increase of 1.98%, 7.1%, 5.42% and 2.53% respectively compared with Faster R-CNN, YOLO v3, YOLO v4 and YOLO v5.
ISSR based genetic diversity and phenotypic characterization in relation to pathogenicity of Macrophomina phaseolina isolates in sesame
Root rot is a serious threat to the sesame crop, causing a significant yield loss in Odisha, India. Ten isolates were recovered from different geographical locations in Odisha, India, to understand the relationship among phenotypic characteristics, virulence, and genetic base of the pathogen Macrophomina phaseolina. Genetic diversity analysis using four ISSR primers generated twenty-seven bands with 71.9% polymorphism. The PIC value ranged from 0.59 to 0.663, with a maximum PIC value in ISSR9. UPGMA grouped isolates into three distinct clusters with 60 % genetic similarity. The ordination of isolates in the dendrogram and PCA analysis showed a consistent relationship of geographic origin with the genetic base of the pathogen. A similar grouping pattern was observed based on phenotypic traits and virulence of isolates. All isolates exhibited a significant variation in phenotype (colony colour, colony type, growth rate, aerial mycelium microsclerotia size, shape, colour, and abundance) and virulence (against cultivar VRI- 1). In addition, a negative correlation was observed between the sizes of microsclerotia with virulence. The findings confirm a considerable variation among the isolates and a strong relationship between phenotypic characteristics, virulence, and the genetic base of the pathogen. Knowledge of these characteristics may help to understand the population structure of the pathogen.
Intelligent Irrigation, Fertilization, Post-Harvest Management with IoT Technology: Challenges and Setbacks
Internet of Things (IoT) has been a serious influence in agriculture since its application to the sector. This paper provides an in depth review of the employment of good technologies in agriculture and elaborates the progressive technologies for good agriculture together with, web of Things, cloud computing, machine learning, and computer science. The application in smart agriculture in crop production and post-harvesting is mentioned. The impact of climate change on agriculture is additionally thought-about. This paper contributes to information by iterating the challenges of good technology to agriculture whereas lightness the problems known from existing framework of smart agriculture. The authors determine several gaps in existing analysis affecting the application of IoT in smart agriculture, and counsel any analysis to boost the current food production globally, to supply higher food management and property measures across the world.
Economic Impact of Sub-Surface Drainage (SSD) technology on sugarcane production
This study has accessed the economic impact of Sub-Surface Drainage (SSD) technology on sugarcane production. The 'with' and 'without' approach was adopted for the collection and analysis of data. 60 sample farmers ‘with SSD’ and 60 sample farmers ‘without SSD’ consisting of total 120 farmers were selected from three tahsils of Sangli district of Maharashtra. The average per hectare establishment cost of SSD system was observed to be Rs. 1,38,183. The per quintal cost of production for adsali sugarcane for with and without SSD farms was Rs. 219 and 279 respectively. The gross return of adsali sugarcane was observed to be 40.77 per cent more on ‘with SSD’ farms as compare to the ‘without SSD’ farms of adsali sugarcane. The per quintal cost reduction for adsali sugarcane observed to be 27.43 per cent on ‘with SSD’ farms over the ‘without SSD’ farms. The per farm gross income accrued from cultivation of adsali sugarcane was Rs. 414675.25 and Rs. 245619.15 on farms with and without SSD, respectively showed an increase of 40.77 per cent in gross returns due to the adoption of SSD technology. While, the farm business income, family labour income, net income were also higher on farms with SSD over the farms without SSD with the tune of 67.98, 86.65and 104.44 per cent, respectively.