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
Effect of irrigation management on growth, yield, water use efficiency and economics of fennel (Foeniculum vulgare Mill)
A field trial was carried out at SKN College of Agriculture, Jobner, Jaipur (Rajasthan) during rabi 2018-19, 2019-20 and 2020-21to find out the effect of different irrigation methods (flood irrigation, sprinkler irrigation and drip irrigation) on growth, yield, water use efficiency and economics of fennel (Foeniculum vulgare Mill.). The experiment comprising of five treatments viz. flood irrigation, sprinkler irrigation, drip irrigation at 0.8 IW/CPE ratio, drip irrigation at 1.0 IW/CPE ratio and drip irrigation at 1.2 IW/CPE ratio. Research findings of present experiment revealed that the drip irrigation treatments brought an additive effect in increasing growth, yield, water use efficiency, quality and economics of fennel. Drip irrigation at 0.8 IW/CPE ratio produced higher plant height (105.33 cm), umbels/plant (27.44), umbellets /umbel (22.40), seeds/umbel (330.73), seed yield (25.56 q/ha), harvest index (30.61%), essential oil (1.70 %), net returns (Rs 135720/ha), B:C ratio (3.43) and water use efficiency (7.22 kg/ha-mm) along with 33.03% of water saving which was significantly superior in the comparison of flood irrigation and sprinkler irrigation.
Genetic Parameters and Characterization of Rice Genotypes under Drought Stress
Two experiments were conducted to evaluate seventeen rice genotypes behavior under normal and water deficit conditions during the 2019 and 2020 rice growing seasons to screen and identify the genotypes and determine the remarkable criteria for selection. The study revealed that environmental conditions, genotypes, and their interaction (GEI) mean squares were found to be highly significant for all physiological and morphological characters studied. Whereas, the genotypes that show high mean values for physiological and morphological characters are tolerant to water shortages. The grain yield plant-1 was investigated under normal and drought conditions, and it was positively significant associated with soluble sugar, peroxidase activity, the number of panicles plant-1, and 100-grain weight under drought conditions. On the other hand, a significant negative correlation was observed with soluble sugar, catalase activity, peroxidase activity, stomatal conductance, Na content, and sterility percentage under both normal and drought conditions, respectively.
EFFECT OF STAGGERED SOWING AND WEATHER FACTORS ON THE SEASONAL INCIDENCE OF HELICOVERPA ARMIGERA (HUBNER) IN CHICKPEA
The field experiment was conducted to confirm the optimum sowing date for chickpea to determine the infestation of H. armigera. It was found that the incidence and population fluctuations of this pest in both successive years were highly dependent on the prevailing weather parameters during the growing season of all three sowing dates. The minimum egg population was recorded as early sown crop on November 15th which was significantly superior over the other dates of sowing. Correlation analysis revealed that maximum and minimum temperatures exhibited significantly positive correlation with egg population of H. armigera in all three dates of sowing in both the year 2019-20 and 2020-21 except crop sown on 15th November and 30th November in 2019-20 in case of minimum temperature, while significant negative correlation was found with evening RH% on 30th November sown crop in 2019-20. The minimum larval population was recorded as early sown crop November 15 while highest larval population was observed with late sowing. The maximum temperature significantly positive correlation with all dates of sowing in both year of research 2019-20 and 2020-21. while evening RH% showed negative significant association chickpea sown on 30th November in the year of 2019-20. Whereas rainfall and rainy days exhibited negative correlation with mean larval population on December 15th in the year of 2019-20.
A Novel Aerial Object Detection Technique Using Deep Learning Method
Object detection in aerial images dataset is a challenging concept because of its dynamic behavior. This proposed work provides a novel way of aerial image detection in high spatial resolution aerial picture land-use/cover mapping using a method that is introduced to deal with the unique properties of aerial photographs, such as frequency domain content variability. Patch detection and description, in particular, are devised to partition and describe diverse sub-regions of objects made up of many homogenous components. In the present work we have proposed the VGG16 and its output is further feed to the Faster RCNN which makes the proposed model a novel work. Furthermore, the proposed bag of feature representation is built using statistics learned from the training dataset about the occurrence of the learning set of the image dataset. The analyses of several patch descriptors show that a mixture of spectral and textural characteristics is a good choice. In addition, to limit the impact of outliers on categorization in test data, a threshold-based technique is used. Experiments with data from aerial images are simulated and results are obtained using MATLAB 2021R software then the results are contrasted with the methods currently in use. The proposed method outperforms the existing work.
Pest Succession and documentation of insect pests and natural enemies fauna in maize ecosystem post- fall armyworm, Spodoptera frugiperda (JE Smith) infestation
Maize fall armyworm, Spodoptera frugiperda is a growing concern in major maize growing regions of the country. Since the invasion of the pest in August 2018, there seems to be a shift in the pest and natural enemy succession in maize crop. While stem borers occupied the central whorls during pre-FAW scenario, fall armyworm tend to occupy the central whorl since invasion of the latter. Following fall armyworm infestation there was a dearth of natural enemy population initially. But as time progressed, associated natural enemies including parasitoids (Telenomus remus, Chelonus sp., Trathala sp., etc.) and predators (carabids, staphylinids, etc.) have gained entry into maize ecosystems in large numbers aiding considerable natural suppression of the pest. A total of 24 insect species including six sucking insects, four defoliators and 12 natural enemies were recorded besides two scavengers. About 15.1 per cent natural parasitism of eggs by Telenomus remus was recorded besides higher numbers of Chelonus sp. during early stages of crop growth (25 – 35 days). Initial natural predation and parasitism are considered as positive signs of increasing natural biosuppression and calls for reduced insecticidal sprays during early crop growth period.