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.
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
The current study used twenty-five okra genotypes to evaluate the genetic variability, heritability, and genetic advance as a percentage of the mean. During rabi 2020-21, all twenty-five genotypes were evaluated in a randomized block design with three replications. For all the features, analysis of variance revealed a significant level of variability across the genotypes, indicating a broad range of variability across the genotypes. Number of fruits per plant has recorded the highest GCV and PCV followed by number of nodes per plant. This suggested that the environment had the least impact on the manifestation of these features. High heritability coupled with high genetic advance as per cent of mean were observed for number of fruits per plant, plant height, fruit length, number of ridges per fruit, 100 seed weight, number of nodes per plant, number of branches per plant, average fruit weight, fruit yield, fruit diameter, number of locules per fruit, days to 50% flowering, peduncle length, stem diameter at final fruit harvest, days to first fruit harvest. While in correlation studies number of nodes per plant followed by number of branches per plant, fruit diameter, number of ridges per fruits, average fruit weight, 100 seed weight has shown positive and high significant association with fruit yield per plant. Elsewhere fruit length has negative and highly significance. Path analysis revealed that positive direct effect on fruit yield per plant per plant was observed by number of nodes per plant, number of branches per plant, fruit diameter, number of ridges per fruit, number of fruits per plant, average fruit weight, 100 seed weight. Whereas fruit length followed by days to 50% flowering, peduncle length, days to first fruit harvesting, stem diameter at final harvest, fruit length has shown the negative direct effect on the fruit yield per plant. As a result, these traits should be prioritised in the selection of high-yielding okra genotypes.
Aiming at the problem of low automation of field transportation, the path planning and tracking control technology of tracked transfer vehicle are studied. A control system based on ROS (robot operating system) platform is designed. The system integrates the information of global satellite navigation system (GNSS) and inertial navigation system (INS), realizes the real-time acquisition of the actual position of tracked transfer vehicle in Hilly and Mountainous Orchard field with high precision, and adopts the fuzzy proportional integral derivative (PID) controller based on parameter optimization preview to realize path tracking. The test results show that under the condition of normal positioning signal, the average lateral deviation of transfer vehicle path tracking is 10cm; When the positioning signal is abnormal, the transverse deviation tracked by the transfer vehicle is 10.56cm, which can achieve the goal of automatic driving of the transfer vehicle in orchards in Hilly and mountainous areas.
Predominantly, the identification of nutrient deficiency in plantain trees are based on visual symptoms. Nutrient anomalies are performed by trained specialists manually. It is a time-consuming process and needs proper attention since the symptoms appear in various parts of the crops. This work proposes an image classification and recognition system that analyses for analyzing nutrient deficiency from their visual appearance. The methodology uses deep learning techniques to analyze the nutritional status of the plant using the digital images of its leaves. Initially, the experiments are carried out for the boron and potassium nutrients deficiency of the plantain trees. The banana nutrient deficiency dataset consists of nutrient-deficient leaf images of plantain trees collected from various cultivation fields of Tamil Nadu. The efficacy of Convolutional Neural Network (CNN) makes them suitable for these kinds of applications. The system has experimented with state-of-the-art CNN architectures such as resnet50 and googlenet for classifying Boron and Potassium deficient plant images. The efficient architecture in terms of classification accuracy is selected for developing the SmartPhone-Centric real time nutrient deficiency detection system for assisting the farmers. The application presents the nutritional status of the plants, when getting the Plantain leaf images through the classification performed by the CNN. In the future, the dataset will expand by collecting the images of other nutrient deficient plants and will improve the classification accuracy with a greater number of images for obtaining more advantageous and expressive results for the real-time use of the farmers.
An agronomic investigation was carried out at Post Graduate Institutional Research Farm, Mahatama Phule Krishi Vidyapeeth, Rahuri, Dist. Ahmednagar, Maharashtra (India) during summer 2018 to study optimum irrigation schedule and the effect of foliar application of potash on growth, yield and quality of summer greengram. The experiment was laid out in split plot design with three replications. The experiment consists of twelve treatments involving four main plot treatments i.e. irrigation schedules at 40, 60, 80 and at 100 mm CPE and the subplot treatments are foliar application of 1 % potash (KNO3) at flowering, at pod development stage and at flowering and pod development stage. The experimental results revealed that, the maximum consumptive use of water recorded under scheduling of irrigation at 40 mm CPE (305 mm), followed by treatment irrigation at 60 mm CPE (223 mm). Among the different treatments, irrigation at 40 mm CPE recorded significantly higher seed yield (13.31 q ha-1) and yield attributes of summer greengram and it was at par with treatment irrigation at 60 mm CPE (13.08 q ha-1). Foliar application of 1% potash (KNO3) at flowering and at pod development stage recorded significantly higher seed yield (12.42 q ha-1) and yield attributes of summer greengram. The study showed results the irrigation at 60 mm CPE and foliar application of 1% potash at flowering and pod development stage to summer greengram found suitable preposition to achieve highest grain yield.
A field experiment was conducted during 2019-20 at the Agronomy Farm, Rajasthan College of Agriculture, Udaipur. The experiment was laid out in a RBD with three replications and twelve treatments viz. T1: Hoeings at 20 and 40 DAS, T2: Power weeding at 20 DAS + hoeing at 40 DAS, T3: Stale seedbed + two hoeings at 20 and 40 DAS, T4: Stale seedbed + power weeding at 20 DAS + hoeing at 40 DAS, T5: Stale seedbed + hoeing at 20 DAS + straw mulch at 30 DAS, T6: Stale seedbed + plastic mulch at sowing, T7: Soil solarization + two hoeings at 20 and 40 DAS, T8: Soil solarization + power weeding at 20 DAS + hoeing at 40 DAS, T9: Soil solarization + hoeing at 20 DAS + straw mulch at 30 DAS, T10: Soil solarization + plastic mulch at sowing, T11: Weed free check and T12: Weedy check. On the basis of experiment result it was revealed that T10 and T6 at all stages of observation and T9 and T5 at 30 DAS and T11 up to 50 DAS were the most effective treatment in reducing weed density and weed dry matter accumulation as compared to other treatments. Like weed dry matter accumulation T10 and T6 gave 100 per cent weed control efficiency at all stages of observation. The highest green cob and fodder yield were recorded in T10 (12.97 and 26.32 tonnes ha-1) which was statistically at par with T9 (12.77 and 24.57 tonnes ha-1, respectively).