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
RAPID RECOGNITION OF SPROUTING POTATO IMAGES BASED ON IMPROVED YOLO V5
Sprouted potato detection is an essential measure before potatoes enter warehouse storage and can effectively reduce the chance of warehouse spoilage of potatoes. How to detect potato health intelligently and efficiently is important to improve the quality of potatoes before they enter the warehouse. To achieve the detection and grading of sprouted potatoes in a variety of complex scenarios, this study proposes a sprouted potato detection algorithm based on an improved YOLOV5 model. The Cross Conv module with improved feature similarity is used to replace the Conv of the original C3 module of YOLOV5, which improves the similarity loss problem in the fusion process and increases the feature expression capability; the SPPF with accelerated space pyramid is used instead of SPP for fusion pooling, which reduces the number of fusion parameters and accelerates the speed of fusion pooling; the 9-Mosaic algorithm is enhanced and optimized to strengthen small target features before the image enters Backbone; then the accuracy is further improved using hyperparametric evolution with genetic evolution anchor points and multi-scale training mechanism, and the experimental results show that: the improved model recognition accuracy reaches a minimum of 90.14%, the average accuracy of the whole class mAP@.5 reaches 88%, and the F1-score is 84%, which is higher than the original YOLOV5 network in the same test dataset The model mAP@.5 index is 7.4% higher than the original model under the same test dataset, which has obvious advantages over the existing model. The real-time sprouting potato image recognition based on improved YOLOV5 proposed in this study has good accuracy and effectiveness, which can basically meet the requirements for establishing automatic potato sorting line and realizing high-throughput and fast potato classification, and provide technical reference for intelligent agricultural equipment in modern agricultural environment
Path tracking algorithm based on speed self-adjusting for rice transplanter
Considering the influence of speed on the path tracking accuracy of transplanter, a path tracking algorithm based on speed self-adjusting is proposed to the improve performance of the linear path tracking of transplanter operating in complex field environments. Firstly, a speed self- adjusting fuzzy controller is established, where the lateral and heading deviations are taken as inputs and driving speed as outputs. Then the look- ahead distance is adjusted online through the adjusted driving speed, and the desired front-wheel turning angle is obtained using a pure pursuit algorithm in combination with the look-ahead distance adaptive strategy. A simulation experiment based on Matlab/Simulink and a field experiment were carried out to verify the performance of the proposed algorithm. The Kubota SPU-68C rice transplanter with the automatic navigation control system was used as a test platform in the field experiment. The results showed that the proposed algorithm could automatically adjust the driving speed of the transplanter according to the real-time deviation. Compared with the pure tracking algorithm, the proposed algorithm can obtain better path tracking accuracy and achieve the linear tracking state faster.
Determining Optimal Gear Ratios of A Two-stage Helical Gearbox with Second Stage Double Gear Sets for Minimal Gearbox Bottom Area
This article introduces an optimization study to find the optimal gear ratios for a two-stage helical gearbox with second stage double gear sets to obtain the minimum gearbox bottom area. To do that, a simulation experiment with six main design parameters is conducted. These parameters include the total gearbox ratio, the face width coefficient of both stages, the allowable contact stress of both stages and the output torque. From the results of the experiment, the influence of the main design parameters on the optimal gear ratio of the first stage u1 was evaluated using Minitab software. In addition, a regression formula to calculate u1 has been given.
Calculating Optimum Gear ratios for Four-stage Helical Gearboxes for Getting Minimum Gearbox Bottom Area
This paper presents the results of an optimization problem to determine the optimal gear ratios of a four-stage helical gearbox to achieve the minimum gearbox bottom area. To do that, a simulation experiment was conducted. In addition, the influence of six main design parameters including the surface width coefficient of the first and second stages, the allowable contact stresses of the first and second stages, and the output torque have been evaluated. Moreover, regression equations to determine the optimal gear ratios of the gearbox have been proposed.
Insecticidal effect of three plant powder against two stored-product pests, Sitophilus granarius (Linnaeus) and Tribolium confusum (Jacquelin du Val).
One of the top pest concerns in food production and storage across the globe, is stored product insects because they cause substantial damage and contamination. Insect infestations induce changes to the storage environment leading to warm, moist conditions which are suitable for fungal growth that further causes hazardous effects. The use of chemical insecticides to control pests can cause toxicity hazards to non-target organisms and serious health problems for humans. From this perspective, we assessed the effect of three botanical powders, Boswellia carterii (Sapindales Burseraceae), Elettaria cardamomum (Zingiberales: Zingiberaceae) and Pistacia lentiscus (Sapindales Anacardiaceae) against two stored-product pests, granary weevil Sitophilus granarius (Coleoptera Curculionidae) and confused flour beetle Tribolium confusum (Coleoptera Tenebrionidae). Both insects were affected by different powder concentrations, time of exposure, and the bioactivity of the plant components. Adult S. granarius were more sensitive to different powder concentrations than those of T. confusum. Concentrations of 15% B. carterii, E. cardamomum and P. lentiscus powders caused 73.3, 80, and 100% mortality of T. confusum after 14 days post treatment, respectively. While the same concentrations of the three botanical powders caused 100, 96.7, and 100% mortality of S. granarius. P. lentiscus was more effective than B. carterii and E. cardamomum. This study showed that B. carterii, E. cardamomum and P. lentiscus powders had biological and toxicological effects against S. granarius and T. confusum. These plant powders are considered safe for human use.