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 (2025)
07 Dec 2025 (Vol - 56 , Issue- 12 )
31 Dec 2025 (Vol - 56 , Issue 12 )
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
Design and Analysis of Energy-saving Forward Deep-rotary Tillage Machine
Rotary tillage operation generally has problems such as shallow tillage depth (8-15cm), low depth of mixed burying of straw, and high power consumption. Increasing the tilling depth by increasing the turning radius of the rotary tiller has problems such as huge mechanism and increased power consumption. In this paper, a deep rotary machine was designed, which could still achieve deep operation (tillage depth of 20cm) when the blade turning radius was reduced. Through the method of simulation and experiment, the article compared the operating performance of the deep-rotating machine and the common tool with increasing rotary radius. The result shown the advantages of the deep-rotating machine in the index of tillage depth and power consumption; the parameter pair of the deep-rotating machine was clarified. For the impact of job performance, the methods of variance analysis and response surface analysis were adopted to explore the relationship between parameters and indicators and fitted mathematical models. Field experiments were carried out and compared with the simulation analysis results. The errors between field test results and simulation analysis were 8.2%, 3.5% and 4.2% in power consumption, soil fragmentation rate and straw coverage rate. The average error in soil vertical displacement was 15.2%, and the longitudinal displacement error was 16.9%. The average error in the vertical distribution of straw is 15.7%.
Research on Mask-RCNN-Based Online Monitoring System for Wheat Impurity Rate and Breakage Rate
A sampling device that can collect high-quality images of wheat grain was designed, and the image was processed by Mask-RCNN algorithm. Then the calculation model which can calculate the impurity rate and breakage rate based on image information was established. Wheat grain images collected in the field were labeled and trained. The trained algorithm can identify the number of wheat kernels, the pixel area of damaged wheat kernels, and the pixel area of impurities in the image. Then the three data were input into the calculation model to obtain the impurity rate and damage rate. This protocol enabled the fast recognition and segmentation of wheat grain images with 90.46% recognition accuracy for intact wheat seeds, 88.85% and 90.32% segmentation accuracy for broken wheat seeds and impurities. The relative error of the method for monitoring the impurity rate and breakage rate of wheat is less than or equal to 9.86%. The research in this paper can monitor the wheat impurity rate and breakage rate when the combine harvester is harvesting wheat, which helps the driver adjust the combine harvester in time to improve the harvesting efficiency, and it can provide a reference basis for intelligent regulation of combine harvesters.
The Levers For Improving Irrigated Durum Wheat Performances: Evidence from Jendouba region in North Tunisia
In Tunisia, despite the outstanding development of the production technologies, the achieved yields of the irrigated durum wheat are still under expectations. This work aims to establish an operational diagnosis of the irrigated durum wheat activity and to identify alternatives for improving its performance. In order to deal with this issue, a field survey was carried out among a sample of 77 farmers cultivating durum wheat. Using CROPSYST software, a crop model was developed and scenarios of good practices were simulated. The results showed that we are able to rise the yields up to 20% and to improve the water productivity and the gross Margin. Hence results proved that matching water intakes and crop rotation constitutes relevant practices to ensure better agronomical and economical performances. The concretization of these paths requires a concerted reflection between the actors to put forward suitable strategies according to the studied context.
Ethnobotanical approaches of local plants of lodhran district, Punjab, Pakistan.
The present study is conducted to gather the native awareness of therapeutic plants of Lodhran District, south Punjab, Pakistan, and documentation of remarkable wild plants. The area hasten been studied before for his purpose. The area is rich in medicinal plans and local people are using I for various ailments. This study was donein 2019. The information was collected through specific questionnaires and field trips and local health care professionals including Hakeems, Daii, and Peer (Spiritual Healers) were interviewed. Documented plants were further identified by Flora of Pakistan. An overall of 66therapeutic plants with 36 families was acknowledged through various field trips and with detailed interviews of the specific people in the community and their local names and folk uses are described along with their dosage forms.
Detection of peanuts mildew based on hyperspectral image
Peanut mildew can produce aflatoxin with strong toxicity. A nondestructive and rapid detection method of peanut mildew based on hyperspectral technology was proposed. Firstly, 600 Dabaisha peanuts purchased in the market were selected, and 200 peanuts were randomly selected for mildew treatment, while the remaining 400 peanuts were kept aseptically. After 30 days, the spectral images of all peanut samples were collected by using the Zhuoli Huanguang Hyperspectral Instrument and the SpaceView was used for black and white correction. Then the ROI of each peanut spectral image was extracted with ENVI5.1 software and the Mean spectral reflection value of the region was calculated to obtain the sample spectral data, the reflection curves of moldy peanuts and healthy peanuts were observed, and it was found that there were significant differences between 500nm-600nm, so the mold visualization of moldy peanuts was carried out within this interval. In order to eliminate the influence of non-quality factor information in the hyperspectral data, three spectral preprocessing methods were adopted to eliminate the noise in the original spectral data. XGBoost, LightGBM and RandomForest algorithms were run to model the characteristic bands and detect the moldy peanuts. In all models, the accuracy of the algorithm could reach 100%, the similarity indexes all reached 1, and the loss was all 0. In terms of FIT_TIME, XGBOOST had the best performance, only requiring 0.042s, which was significantly different from other algorithms. The results showed that XGBOOST model was the most suitable algorithm for detecting peanut mold. This study provides a strong theoretical basis and technical support for the monitoring and identification of healthy and moldy peanuts in peanut processing industry.