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
Awareness Level of Farmers Towards Direct Seeded Rice Technology in Haryana State of India
The current study was conducted in Haryana's districts of Ambala and Kurukshetra in 2020 and 2021, to assess farmers' awareness of Direct Seeded Rice (DSR) cultivation technology in eight villages in these districts in order to improve resource use efficiency and lower rice cultivation costs. Empirical data was obtained using a well-structured and pre-tested interview schedule and analysed using appropriate statistical procedures utilising the Statistical Package for Social Sciences 26th version (SPSS). According to the findings, two-thirds of the respondents (41.25%) had a poor degree of knowledge regarding DSR technology, followed by medium and high levels of expertise. Indeed, the government and non-governmental groups should arrange more awareness programmes to raise the level of understanding of DSR technology in order to sustain rice production in Haryana. Personal attributes like education, land holding, farm equipment availability, mass media exposure, extension contact, economic motivation, risk orientation, and innovativeness all showed a positive and significant link with their awareness level about DSR technology at the 0.05 level of probability.
Oil content and fatty acid composition of soybean seed influenced by various Zinc sources under fine typic Kanhapludalf soil
Experiments were conducted in two locations under the acidic soil conditions of Nagaland. Growth, yield, yield attributes and quality of soybean were recorded, analysed and computed statistically during 2019, 2020 and pooled respectively. It was observed that the parameters such as plant height, no. of nodules plant-1, fresh weight of nodules, dry weight of nodules, seeds pod-1, pods plant-1, seed and stover yield, protein content and yield, oil content and yield and soil fertility after harvest were significantly increased @ 5 kg ha-1 ZnSO4 H2O + RDF during both the experimental years as well as in pooled respectively. The fatty acids composition in soybean such as palmitic, stearic, oleic, linoleic and linolenic acid were also found to be significantly increased @ 5 kg ha-1 ZnSO4 H2O + RDF among the various Zn sources applied. From the experiment conducted, it was observed that soybean responded well to application of Zinc sources in balanced doses.
A Bias Gantry Profiling Boom Sprayer for Orchard Protection
An air-assisted sprayer sends liquid medicine to a canopy of orchard plants for protection. However, the inherent drift in this method lowers the pesticide utilization. To meet the gardening requirements of a short-anvil densely planted apple orchard, a profiling boom sprayer was designed, and the operation requirements and prototype operation parameters of plant protection were determined. The droplet depositions in the upper, middle, and lower layers of the targets and in the inner, middle and outer rings were analyzed in field experiments. The standard deviations of the droplet deposition coverage rates on free, slender, and high spindles at different heights were 4.43, 2.82, and 5.29, respectively, and those of the droplet deposition densities were 5.97, 4.98, and 6.15, respectively. All p-values exceeded 0.05, indicating that droplets from the outer ring were uniformly distributed at different canopy heights. The average droplet deposition density exceeded 150 grains·cm-2 in the outer and center rings of the three tree-shaped targets, and reached 100.60 grains·cm-2 in the inner ring. The droplet deposition coverage rates on the free, slender, and high spindles in the inner ring were 37.41%, 36.69%, and 35.47%, respectively, indicating that the droplet penetration ability of the profiling boom sprayer meets the requirements of plant protection. The developed profiling boom sprayer has improved the inherent serious drift problem of the air blower sprayer, and has provided inspiration for the research and development of orchard plant protection machinery.
Quality Protein Maize Hybrids (Zea mays L.) response on Growth parameters under Different Plant Populations and Nutrient Management Practices
A field experiment was conducted at Udaipur during Kharif season of 2016 to study the Response of Quality Protein Maize Hybrids (Zea mays L.) on Growth parameters under Different Plant Population and Nutrient Management Practices. Results revealed that hybrid HQPM-5 recorded higher plant population, plant height, dry matter accumulation at 25, 50 75 DAS and at harvest (19.70, 60.94, 197.51 and 214.47 g plant-1), CGR between 25-50 DAS (15.00 g m-2 day-1) and 50-75 DAS (50.07 g m-2 day-1), RGR, LAI over PQMH-1. Maize hybrid HQPM-5 attend significantly early tasseling and silking than PQMH-1. 1,00,000 plants ha-1 recorded higher DMA, CGR, RGR, LAI over 83,333 plants ha-1. Among various nutrient management practices STCR recorded highest plant height, DMA, CGR, RGR, LAI over SSNM and RDF, respectively.
Enhanced Flower Pollination Algorithm for Edge Detection and its Application for Segmentation of Banana Leaf Disease Image
Edge detection algorithms play a vital role in image processing, computer vision, and machine vision. There is a huge demand for efficient edge detection algorithms for identifying the exact region of interest in an image. The nature-inspired metaheuristic algorithms are more promising than traditional algorithms owing to their stochastic characteristics. The concept of the Flower Pollination Algorithm (FPA) has recently gained much attraction due to solving several complicated optimization problems. In this study, an efficient edge detection algorithm has been developed using FPA for identifying edges in an image. The proposed FPA is capable of identifying the edges with minimal parameterized values, and the parameters are initialized automatically using the concept of maximum entropy and polynomial curve fitting distribution. The performance of flower pollination based edge detection algorithm on ground truth images was compared with other existing methods like Sobel, Canny, Ant Colony based edge detection method, PSO method, and fuzzy-genetic based edge detection methods using Receiver Operating Characteristics (ROC) curve and Area Under Curve (AUC) method. The proposed method performed better compared with other existing methods and had significantly high ROC and AUC indices. Also, the performance of the proposed algorithm was tested on real-time banana leaf disease images and compared with other existing methods in terms of the Shannon entropy index. The segmented images of banana leaf disease through enhanced FPA showed significantly lower mean entropy value, indicating extra-ordinary accuracy and negligible uncertainty of the enhanced FPA. The proposed FPA seems to be promising in developing image processing modules for crop disease diagnosis.