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AMA, Agricultural Mechanization in Asia, Africa and Latin America

Submission Deadline
26 Sep 2023 (Vol - 54 , Issue- 09 )
Upcoming Publication
30 Sep 2023 (Vol - 54 , Issue 09 )

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

Screening of chrysanthemum varieties for postharvest leaf longevity and vase life on physiological and molecular basis

Paper ID- AMA-21-10-2022-11748

Twenty four varieties of chrysanthemum were screened for leaf longevity based on phenotypic and physiological traits. The experiments were laid out in Completely Randomized Design (CRD) consisting of 24 varieties with 3 replications. Out of 24 varieties minimum leaf longevity was recorded in cv. Pusa Shwet, while maximum in cv. Pusa Guldasta. Earliest leaf browning was recorded in cv. Raja whereas cv. Tata Century took maximum days for leaf browning. Maximum vase life was recorded in cv. Tata Century while minimum in cv. Yellow Reflex. Chlorophyll content and relative water content found to decrease with increase in storage after harvest. Maximum total chlorophyll and chlorophyll a content was recorded in cv. Pusa Guldasta while minimum in Pusa Shwet. However, Maximum chlorophyll b content was recorded cv. Jaya and minimum in cv. Raja. Maximum relative water content was recorded in cv. Tata Century while minimum in cv. Pusa Shwet. Thus among 24 varieties selected; two varieties viz., Tata Century and Pusa Guldasta performed best in terms of leaf longevity, vase life, chlorophyll content and relative water content.

Inosperma cervicolor from the Himalayan range of Pakistan; First record from Asia on the basis of phenotypic and phylogenetic analysis

Paper ID- AMA-21-10-2022-11747

Inosperma is a rarely studied genus in Pakistan. During the survey of mushrooms in Pakistan, the author comes across a species of Inosperma cervicolor from the Himalayan moist temperate region of Pakistan. Originally, it was described from Europe. This species resembles with European collection due to free lamellae and non-dextrinoid yellowish-brown, oblong, smooth spores. Striation on the stipe and pileus represents the distinguished character of the taxa. The species is provided with a full morpho-anatomical and phylogenetic description, line drawings of the micro characters, discussion of related and similar species, and molecular data. Here, it is reported as the first time from Asia.


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.

Model Prediction for Dehydration of Pumpkin Using Artificial Neural Networks (ANN)

Paper ID- AMA-18-10-2022-11741

This study was aimed at finding an optimum pre-treatment method and parameters for convective drying of pumpkin for preparation of flour to maximize the retention of nutrients, for the production of pumpkin flesh powder. Artificial Neural Network (ANN) was employed for modeling of drying conditions to obtain powders with desired properties. For this, the pumpkin (Cucurbita moschata) flesh was subjected to blanching (90ᵒ C for 5 minutes) and citric acid treatment (0.1% acid for 30 minutes). Then they were subjected to cabinet drying at temperature and drying time varied at five levels. ANN modeling was performed using three different training functions and at two levels of hidden layers. The physico-chemical analysis had shown that blanching treatment came out with best physical properties and higher retention of nutrients when compared to the control and acid treated samples. The values of co-efficient of determination (R2), Root mean square error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to determine the training function and hidden layer combination for each response variable for their prediction with highest accuracy.