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
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:
The experiment was carried out to assess the genetic variability, character association and path analysis infifteen gynoecious parthenocarpic hybrids/lines of cucumber under polyhouse (fifty percent shade/naturally ventilated) conditions. The genotypic and phenotypic coefficient of variations were highest for nodal position of first female flower (48.44 and 51.04),number fruits (40.99 and 41.24), fruit weight(26.40 and 27.39) and fruit yield per plant(38.22 and 38.93). High heritability coupled with High genetic advance as per cent mean was observed for most of the characters, indicating that these characters are controlled by additive gene action. The fruit yield per plant showed significant positive correlation with an number of fruits per plant (0.819 and 0.825), plant height (0.302 and 0.303) and significant negative correlation with days to fifty percent flowering (-0.682 and -0.707), nodal position of first female flower (-0.652 and -0.716) and days to first female flower (-0.373 and -0.379) both at phenotypic and genotypic. maximum positive direct effects towards fruit yield per plant was contributed by days to fifty percent flowering, number of fruits per plant and fruit weight were the most influencing factors. The hybrids namely Vally Star, Multi Star, Silyon and Pune collection were the best with respect to earliness, superior quality as well as maximum yield.
The present experimentwas carried out at Central Horticultural Experiment Station, ICAR- CIAH Godhra, Gujarat for three consecutive growing seasons during 2020, 2021 and 2022 to evaluate the 56 diverse cluster bean types for eight quantitative traits viz., plant height, number of pods per plant, pod length, pod girth, pod weight, Days to first flower, days to fist harvest and pod yield per plant. The maximum inter cluster D2 value was observed between cluster II and cluster V (35.05) followed by cluster V and cluster VII (34.22) which showed the wider genetic diversity among the genotypes of these clusters. The contribution of individual character to genetic divergence was assessed and found that days to first flowering (34.48%) contributes maximum to the total genetic diversity followed by pod length (28.31%), pod yield per plant (15.06%), plant height (10.65%) and number of pods per plant (10.58%). The various patterns of clusters shows the valuable traits for attaining high pod yield was present in cluster V followed by cluster VIII. The cluster V having higher mean values with respect to pods per plant, pod girth, pod weight and pod yield per plant. Whereas, the cluster IV having higher mean values for earliness in the days to first flowering and days to first harvest.
In manufacturing technology, material handling system with higher performance can be improved the productivity of goods. The main purpose is to increase the productivity of the transporting product finished goods to the warehouse. Automated Guided Vehicles (AGVs) are used commonly for transporting items in manufacturing. AGV is a driverless vehicle which transports goods and materials throughout a facility in most cases by either following a wire guide path embedded in the floor. Analyzing many studies with the meta-analysis method is one way to determine the importance of AGV productivity. Meta-analysis is a statistical tool for effects from a collection of previous studies addressing same research subject. In this study, by combining data from the literature, a meta-analysis study was conducted on the importance of AGV productivity. An effect size had been calculated for the utilization of AGVs by using Comprehensive Meta-Analysis (CMA) program. The result for AGV was figured out random effect size with a value of 0.774.
A field experiment was conducted during kharif and rabi 2021-2022 at research farm of College of Post Graduate Studies in Agricultural Sciences, Umiam, Meghalaya with the objective of developing fertilizer adjustment equations and quantifying doses for achieving targeted yield of potato (Solanum tuberosum L.) by using soil test crop response (STCR) approach. At first, soil fertility gradient stabilizing experiment was conducted by which different fertility strips have been created and maize was grown. Based on yield difference and soil available nutrients, it has been proved that fertility gradient has been created with different levels of fertility which was being followed by a test crop experiment on potato. Response of potato to four levels of N, P, K and FYM under different fertility levels was studied. By using soil available nutrients, tuber yield and total nutrient uptake, basic parameters for development of equation have been calculated which are nutrient requirement (NR) were reported as 0.55, 0.26 and 0.93 kg q-1 for N, P and K, respectively, contribution of nutrients N, P, and K from fertilizers (% CF) was found as 33.32, 15.72 and 91.46 %, from soil (% CS) as 8.30, 46.21 and 17.74%, from organic matter FYM (% CFYM) as 11.33, 7.10 and 10.35%. Hence by following the prerequisites as mentioned above, the targeted yield equations can be developed.
Emotional detection (EDR) is a method used to detect and visualize a person's emotions through a combination of technical skills, such as facial recognition, speech and voice recognition, bio sensing, machine learning, and pattern recognition. This method is used in software that allows the system to examine emotions on a person's face through sophisticated photo streaming. This paper represents the creation of a tool using which human information can be abstracted from a man using his facial expression with FEELTECT app ing face detection technologies, resulting in the best location as our aid. The android application acts as an intermediate medium and has been made using app development technologies. It has a user-friendly and convenient user interface. The aim is to see if emotion recognition technology has the potential to improve the quality of human-computer interaction. The data extracted from the application is filtered utilise appropriate results based on the user's expression.