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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.

Submission Deadline
24 Nov 2022 (Vol - 53 , Issue- 11 )
Upcoming Publication
30 Nov 2022 (Vol - 53 , Issue 11 )

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:

Azerbaijan Medical Journal Gongcheng Kexue Yu Jishu/Advanced Engineering Science Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Interventional Pulmonology
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

Impact Of Crop Establishment Methods and Weed Management Practices on Weed Dynamics and Yield of Rice

Paper ID- AMA-24-10-2022-11754

A field experiment was conducted at Agronomy Main Research Farm, Department of Agronomy, Orissa University of Agriculture and Technology, Bhubaneswar, Odisha, India during kharif season of 2016 and 2017 on ‘Impact of Crop Establishment Methods and Weed Management Practices on Weed Dynamics and yield of rice”. The field experiment was laid out in a split plot design with three replications by taking 24 treatment combinations with four crop establishment methods in the main plot, viz., M1: direct seeded rice (DSR), M2: wet seeded rice (WSR), M3: non-puddled transplanted rice (NPTR), M4: puddled transplanted rice (PTR) and six weed management practices in the sub-plot, viz., W1: weedy check, W2: Bensulfuron methyl 0.6% + Pretilachlor 6% ((pre emergence(PE)) 0.660 kg ha-1 + hand weeding (HW) at 30 DAS/T, W3: Bensulfuron methyl 0.6% + Pretilachlor 6% (PE) 0.495kgha-1 + HW at 30 days after sowing/transplanting (DAS/T), W4: Bensulfuron methyl 0.6% + Pretilachlor 6% (PE) 0.495kgha-1 + Bispyribac-Sodium (post emergence (POE)) 0.025 kg ha-1 at 15 DAS/T, W5: Cono weeding (CW) at 15 DAS/T + hand weeding 30 DAS/T, W6: brown manuring/ green manuring. Puddled transplanted rice recorded highest yield with lowest weed incidence and direct seeded rice recorded lowest yield with highest weed incidence among different establishment methods. While, application of Bensulfuron methyl 0.6% + Pretilachlor 6% (PE) @0.660 kg ha-1 +HW at 30DAS/T recorded highest yield with lowest weed incidence.


Paper ID- AMA-22-10-2022-11751

The National Watershed Development Projects for Rainfed Areas (NWDPRA), with a broad objective of resource management for improving agricultural productivity and production to biomass on a sustainable basis and restoring ecological plans in rainfed areas, was launched in 1990–1991 in two Union Territories and 25 States of India. However, it has been put into practice in the India state of Odisha since 2001 with the primary goal of enhancing the quality of life of underprivileged groups, notably tribal women, by fostering chances that will help them raise their standard of living. The current study, which was conducted in the Badasahi and Saraskana blocks of Mayurbhanj as well as the Champua and Jhumpura blocks of Keonjhar district in the Indian state of Odisha, has been designed to analyze socioeconomic characteristics, the degree of involvement, changes in knowledge, skill, attitude, and developments from the farm women for the effective implementation of watershed programmes. The sample size for the study was 192 farm women from 24 watersheds. In order to analyze the data and determine the outcome, statistical tools like percentage, mean score, standard deviation, co-efficient of variation, correlation coefficient, test of significance, critical ratio test, multiple regression, stepwise regression, and path analysis were used. According to the study, the majority of respondents had a relatively middle socioeconomic background. In contrast to harvesting and post-harvest management, which had better associations and significantly influenced development, variable education, extension contact, social participation, possession of agricultural implements, change in knowledge of field crops, and income-generating activities. It had a negative attitude toward institutional arrangement, funding pattern, community organization, planning, programme implementation, and formulation.

Preference Direct Seeding Over Transplanting Technology in Rice during Lockdown Period in Haryana.

Paper ID- AMA-22-10-2022-11749

Paddy sown with conventional puddled transplanting method is being considered as serious issue nowadays in view of climate change as it is more water & labour intensive technology. DSR is a feasible alternative to solve above issues so the present study was conducted in Ambala and Kurukshetra districts of Haryana, aiming at examining the preference of Direct Seeding (DSR) over Trans-Planting/Puddled Rice (TPR) and constraints experienced by farmers in its adoption during Lockdown for COVID-19 control in 2020. Empirical data was collected in 2020 & 2021, personally from 160 respondents, consisting 20 farmers from eight villages through a precisely & designed interview schedule and analyzed using standard methodology. According results majority of the respondents (78.75%) had fallen belonged to Low to Medium and 21.25 percent farmers had found to High category of overall adoption level of DSR technology during paddy season i.e. 2020 kharif, Whereas it was found for the Kharif 2021, most of farmers (88.12%) had a Low to Medium and only 11.88 percent respondents belonged to High category of overall adoption level of DSR that means the adoption of DSR among farmers, in 2020 was a little higher than of 2021 in other words farmers preferred DSR over TPR as they had been faced labour scarcity during COVID-19 pandemic in 2020, which derived them to go for DSR adoption in some extent, rather than TPR and constraint of Non availability of DSR machine at the time of sowing faced the most in lockdown as machine requirement had increased among farmers due to low availability of labour for transplanting during lockdown, Weed infestation, Insufficient knowledge of DSR cultivation, The occurrence of rain before germination were also serious obstacles in adopting DSR.


Paper ID- AMA-21-10-2022-11745

A field experiment was conducted at Regional Agricultural Research Station, Palem, Nagarkurnool, Southern Telangana Agro Climatic Zone of Telangana State during kharif 2018 and 2019 to study the influence of different sowing dates and integrated nutrient management on the soil microbial population, yield and economics of super early pigeonpea. The experiment was laid out in strip plot design for pigeonpea in kharif 2018 and 2019 with 3 main treatments i.e., M1 (1st July), M2 (20th July) and M3 (10th August) and four integrated nutrient management practices as sub treatments viz., S1: 75 % RDF, S2: 75 % RDF + FYM enriched with microbial consortia (1 tonne ha-1), S3: 100 % RDF and S4: 100 % RDF + FYM enriched with microbial consortia (1 tonne ha-1) and replicated thrice. Significantly, higher soil bacterial population observed at 50 per cent flowering stage was 67.3 and 73.3 x 105 cells g-1 and also at harvest stage was 53.7 and 60.3 x 105 cells g-1 during 2018 and 2019 respectively for July 1st sown super early pigeonpea. Highest seed and stover yield were 739 and 3402 kg ha-1 during 2018, 779 and 3527 kg ha-1 in 2019 with the July 1st sowing. Similar trend was also seen in the economics, the highest gross ( 42872 ha-1 in 2018 and 46720 ha-1 in 2019) and net returns ( 19472 ha-1 in 2018 and 23320 ha-1 in 2019) were realized with M1 (July 1st) treatment. The BCR was also found highest (1.83 in 2018 and 1.99 in 2019) with the July 1st (M1) sowing over the July 20th (M2) and August 10th (M3) sowing dates. In the sub treatments S4 (100 % RDF + FYM enriched with microbial consortia @ 1 tonne ha-1) reported highest bacterial population with 85.6 and 90.3 x 105 cells g-1 in 2018 and 2019 during 50 per cent flowering stage. At harvest stage also similar trend in the bacterial population was observed in the sub treatments. The bacterial population 69.2 and 76.2 x 105 cells g-1 recorded in 2018 and 2019 years respectively for application of 100 % RDF + FYM enriched with microbial consortia @ 1 tonne ha-1(S4). The sub treatment S4 revealed highest seed and stover yield with 724 and 3368 kg ha-1 in 2018 and 758 and 3453 kg ha-1 in 2019. Similarly highest gross ( 41973 ha-1 in 2018 and 45500 ha-1 in 2019) and net returns ( 16323 ha-1 in 2018 and 19850 ha-1 in 2019) were realized with application of 100 % RDF + FYM enriched with microbial consortia @ 1 tonne ha-1(S4) compared to the INM sub treatments. BCR recorded significantly highest for the sub treatment S3 (100 % RDF) was 1.84 and 1.77 in 2018 and 2019 consecutive years and it was on par with S4 (100 % RDF + FYM enriched with microbial consortia @ 1 tonne ha-1).

Classification of Black Tomato Ripeness using YOLOv4

Paper ID- AMA-20-10-2022-11744

Black tomatoes are typically classified according to their ripeness immediately after harvesting to maintain optimal quality and minimize the loss caused by uneven ripening. The ripeness of black tomatoes is traditionally evaluated either visually or using a colorimeter. The visual observation technique is time- and labor-intensive and may yield unreliable results, and the colorimeter-based technique can be implemented for only a small number of tomatoes. To address these problems, this paper proposes a method to effectively classify the ripeness of black tomatoes based on machine vision and YOLOv4. A total of 4,080 digital images of Black Change, a variety of black tomatoes, were collected by capturing the top, bottom, left side, and right side of each sample in illuminance conditions of 570 lx, 1,240 lx, and 2,780 lx. The results showed that the model trained with images gathered under a single illuminance condition could effectively classify the ripeness for images with the same condition. When images with mixed lighting conditions were used, the model achieved a classification performance of nearly 100%. However, its performance deteriorated when the model trained with an independent illuminance condition applied to the images for other conditions. The model trained with over 1632 images in mixed illuminance conditions for over 3000 iterations achieved a classification accuracy of at least 96.00%, and time required for image collection, labeling, and training was minimized.