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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. Lizi Jiaohuan Yu Xifu/Ion Exchange and Adsorption Fa yi xue za zhi Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology Research Journal of Chemistry and Environment

Submission Deadline
21 May 2024 (Vol - 55 , Issue- 05 )
Upcoming Publication
31 May 2024 (Vol - 55 , 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

EFFECT OF ORGANICS ON CONTENT OF MACRO AND MICRO NUTRIENTS FROM LEAVES THROUGH DRIP IRRIGATED ONION (Allium cepa L.) UNDER ORGANIC CONDITION

Paper ID- AMA-13-12-2023-12780

An experiment was conducted on medium black clayey soil at organic plot, Krishi Vigyan Kendra, JAU, Amreli (Gujarat) during rabi seasons of 2018-19 and 2019-20 to evaluate cow-based bio-enhancers and botanicals for organic cultivation of onion (var. GJRO-11). Ten treatments comprising Panchagavya as foliar spray @ 3% at 30, 45 and 60 DAT + FYM @ 5 t ha-1, Jivamrut as drenching @ 5% at 15, 30 and 45 DAT + FYM @ 5 t ha-1, Cow urine as foliar spray @ 3% at 15, 30 and 45 DAT + FYM @ 5 t ha-1, Seaweed extract as foliar spray @ 3.5% at 30, 45 and 60 DAT + FYM @ 5 t ha-1, Banana sap as foliar spray @ 1% at 30, 45 and 60 DAT + FYM @ 5 t ha-1, Vermiwash as foliar spray @ 2% at 45 and 60 DAT + FYM @ 5 t ha-1, Enriched vermi compost @ 2 t ha-1 + FYM @ 5 t ha-1, Vermi compost @ 2 t ha-1 + FYM @ 5 t ha-1, FYM @ 20 t ha-1 and Control (Absolute) in randomized block design with three replications. The experiment results on the basis of two years pooled mean revealed that application of FYM 20 t ha-1 followed by Panchagavya as foliar spray @ 3 % at 30, 45 and 60 DAT + FYM @ 5 t ha-1, Jivamrut as drenching @ 5% at 15, 30 and 45 DAT + FYM @ 5 t ha-1, Enriched vermi compost @ 2 t ha-1 + FYM @ 5 t ha-1, vermi compost @ 2 t ha-1 + FYM @ 5 t ha-1 were found superior in nutrient content.

Prediction and Classification of Chlorophyll Contents to Determine the Severity Levels of White Root Disease Infection on Rubber Leaves

Paper ID- AMA-13-12-2023-12779

White root disease (WRD) is one of the most serious infection problems in rubber plantation. At early infection stage, it is very difficult to diagnose the disease because infected trees do not exhibit any symptoms. Early detection of WRD is vital for farmers to initiate early treatment as well as to control the spread of the disease. Thus, this study investigated the feasibility of using spectroscopic method for early detection of WRD disease from rubber leaf samples. A total 50 rubber leaf samples representing five severity levels namely, healthy, light, moderate, severe and very severe infection were used in this study. Spectral data of the leaf samples was collected using visible shortwave near infrared (VSNIR) spectrometer. After the spectral measurement, chlorophyll content of the leaves was measured using SPAD meter. Partial least square (PLS) regression method was used to develop both calibration and prediction models for calibrating the spectral data with chlorophyll content. Artificial neural network (ANN) classifier was used to categorise the spectral data into the respective severity levels. This study found that the value of coefficient determination (R2) and root mean square error of calibration (RMSEC) were 0.99 and 0.56, respectively. For prediction model, the value of R2 and root mean square error of prediction (RMSEP) were 0.99 and 0.82, respectively. The ANN classifier yielded good classification accuracy of 90%. In conclusion, this study has provided a reliable basis to employ VSNIR for early detection of disease severity in rubber plantation.

Automated detection of Breast Cancer after Neoadjuvant Chemotherapy (NAC) complete response using deep neural networks with X-ray images

Paper ID- AMA-12-12-2023-12774

This study explores the application of Convolutional Neural Network (CNN) models in predicting neoadjuvant chemotherapy (NAC) responses through Magnetic Resonance Imaging (MRI) data in breast cancer patients. It aims to enhance prognosis monitoring by leveraging AI-driven image analysis, providing a computational interface for detailed radiological data analysis. Utilizing a dataset encompassing patients' radiological responses post-NAC, a CNN-based model has been developed using the YOLO algorithm. The model's predictive success with new data aimed to refine and improve its performance. The study demonstrates the potential of AI-driven deep learning models, particularly CNNs, in effectively predicting treatment responses through radiological data analysis. This approach combines detailed analysis of radiological data with a computational interface, aiming to aid physicians in their research efforts. The developed CNN model, utilizing the YOLO algorithm, achieved notable performance metrics in predicting neoadjuvant chemotherapy (NAC) responses in breast cancer patients. The developed CNN model, leveraging the YOLO algorithm, achieved significant performance metrics in predicting neoadjuvant chemotherapy (NAC) responses in breast cancer patients with an average precision of 71%, an average accuracy of 70.51%, and an Intersection over Union (IoU) value of 67.00%. The developed interface, implemented using the Python Tkinter library, provided rapid prediction results for new MRI images, completing predictions in under one second. This interface integrated the trained model's weight file, facilitating prediction in various hospital systems with minimal hardware capacities. This research underlines the significance of machine learning and deep learning in interpreting heterogeneous medical data, particularly in medical imaging analysis. The utilization of AI-based models doesn't aim to replace clinicians but serves as a collaborative tool to reduce workload and enhance diagnostic accuracy. While medical imaging methods have significantly advanced, this study emphasizes the potential of AI-driven models in exploiting invisible image features to improve predictive capabilities.

Selection Indices in Vegetable Cowpea (Vigna unguiculata)

Paper ID- AMA-12-12-2023-12771

To create selection indices, fifty various vegetable cowpea genotypes were assessed for twelve traits. Characters were taken into consideration as selection index criteria if they had a desirable correlation as well as moderate to high direct effect on green pod yield per plant. For the purpose of generating selection indices, the green pod yield per plant (X1) and its four components including pod length (X2), number of pods per cluster (X3), number of pods per plant (X4) and ten pod weight (X5) were identified. The selection index based on single character was efficient over straight selection for green pod yield per plant and showed maximum gain and relative efficiency. The efficiency of selection increased with the inclusion of more number of characters in the index. The index based on five characters viz., green pod yield per plant, pod length, number of pods per cluster, number of pods per plant and ten pod weight recorded highest genetic advance and relative efficiency followed by green pod yield per plant, pod length, number of pods per plant and ten pod weight.

Genetic variability and diversity in vegetable cowpea (Vigna unguiculata)

Paper ID- AMA-11-12-2023-12770

The experiment was conducted to evaluate 50 genotypes of vegetable cowpea to know the magnitude of genetic variability, heritability, genetic advance and genetic divergence. Phenotypic observations were meticulously recorded for days to 50 per cent flowering, days to first green pod picking, number of primary branches per plant, plant height (cm), pod length (cm), pod width (cm), number of pods per plant, number of seeds per pod, number of pods per cluster, number of clusters per plant, ten pod weight (g) and green pod yield per plant (g). The magnitude of PCV was slightly greater than GCV which revealed that very little influence of environmental variation was observed on all the characters and stated that a sufficient amount of variability was noticed. The high genotypic coefficient of variation and phenotypic coefficient of variation was observed for plant height, green pod yield per plant, ten pod weight and number of pods per plant. For pod length, number of seeds per pod, plant height, ten pod weight, green pod yield per plant, number of primary branches per plant, number of pods per plant, number of clusters per plant, and number of pods per cluster, estimates of high heritability along with high genetic advance expressed as a percent of mean were noted. Utilizing a cluster analysis, the fifty genotypes were categorized into six distinct clusters. The maximum inter-cluster distance was found between clusters IV and VI. In this context, genotypes from cluster IV and VI should be selected as parents in hybridization programme as they showed highest genetic diversity.