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 aim of present investigation was to screen the microorganisms isolated from various samples such as soil, cattle dung and decaying woody material capable of degrading cellulose. A total 43 bacterial and 16 fungal isolates were retrieved from three different samples by serial dilution plating technique using carboxymethylcellulose (CMC) agar medium. All the bacterial and fungal isolates were screened for cellulase production on the basis of zone of hydrolysis on CMC agar medium. Out of 43 bacterial isolates, 13 bacterial isolates showed zone formation and three isolates i.e. SB2, SB4 and SB10 showed maximum zone of hydrolysis. Out of 16 fungal isolates, 10 fungi showed the zone formation on CMC agar medium and WF1 showed maximum zone. Cellulase activity of the isolates showing maximum zone i.e. SB4 and WF1 was determined and found to be 276.83 IU/mL and 230.62 IU/mL, respectively.
To address the problem that the basic convolutional neural network is susceptible to background interference and weak expression of important features in recognizing bollworm in apple orchards, an identification method of bollworm in apple orchard based on MC-Mask R-CNN (Mish and CBAM - Mask R-CNN) is proposed. Firstly, on the basis of Haar's traditional neural network, the apple orchard images collected by multiple sites are initial segmented iteratively. The bollworm individual image samples are extracted, and the sample is expanded in multiple ways to obtain the expanded sample data set for deep learning. Secondly, an MC-Mask R-CNN feature extraction network is built and the Mish function is selected as the activation function to avoid vanishing gradient and gradient explosion during back propagation. And meanwhile, an attention mechanism module CBAM combining channel attention and spatial attention is introduced to improve the model's ability to express the characteristics of apple orchard bollworm, which is conducive to extracting deep feature information. Lastly, the Mask R-CNN is used as the control model and the recognition average precision is used as the evaluation index. The ablation test results show: integrating both the Mish activation function and the attention mechanism module into the feature extraction network can improve the recognition average precision of the model; the recognition average precision of the proposed MC-Mask R-CNN model reaches 97.69%. Contrasted with the Mask R-CNN, the recognition average precision is 5.08% higher. The results indicate that MC-Mask R-CNN can accurately and effectively identify apple orchard bollworm and provide technical support for the green protection and control of apple orchard diseases and insect pests.
The present investigation was carried out at experimental research farm of Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu at Chatha during 2019-20 and 2020-21. Eight genetically diverse cultivars of strawberry were selected based on their genetic value and crossed through hand self-pollination and caging methods of pollination to produce inbreed lines of cultivars. After crossing of genetically superior parent by two different pollination methods viz., hand self-pollination and pollination by caging, Nabila showed best results among different cultivars. In hand self-pollination, fruit set after 21 days was found to be maximum in Nabila cultivar i.e. 83.32 % and 85.40 % within two growing season, respectively. Simultaneously, cultivars Nabila showed maximum fruit retention (82.90 % and 84.63 %), fruit weight (30.12 g and 31.16 g), fruit length (38.60 mm and 42.70 mm), fruit breadth (43.20 mm and 44.22 mm), number of achenes per fruit (411.75 and 435.75), achene density (17.77 cm2 and 15.66 cm2), respectively. However, cv. Jutog Special showed lowest performance among all these parameters. In self-pollination by caging, fruit set after 21 days was found in cv. Rania (60.32 % and 61.44 %), fruit retention in cv. Nabila (62.18 % and 64.70 %), fruit weight in cv. Nabila (16.49 g and 17.26 g), fruit length in cv. Rania (25.86 mm) in 2019-20 and cv. Nabila (28.13 mm) in 2020-21, fruit breadth in cv. Nabila (16.16 mm and 16.26 mm), number of achenes per fruit (246.32 and 251.14) and achenes density (22.77 cm2) in 2019-20 and 24.06 cm2 in Sweet Charlie in 2020-21. However, maximum fruit set after 21 days were recorded by hand self pollination methods which resulted into maximum fruit retention, fruit weight, fruit length, fruit breadth etc. as compared to self pollination through caging. Hand self-pollination methods is best methods to develop fruit under controlled condition and ensure the pollination in strawberry cultivars to develop inbred lines.
A survey was carried out to study the correlation between available soil nutrients with growth, flowering and physico-chemical characteristics of 110 aonla fruit orchards of Akhnoor, Raya and Purmandal areas of Jammu subtropics during 2016 and 2017. The soil nutrients status in these aonla orchards varied from 79 to 536.50 kg/ha nitrogen (N), 4.65 to 17.57 kg/ha phosphorus (P), 36.65 to 235.95 kg/ha potassium (K), 6.3 to 13.81 mg/kg sulphur (S), 0.31 to 7.18 mg/kg zinc (Zn), 1.37 to 16.76 mg/kg iron (Fe), 0.34 to 4.8 mg/kg copper (Cu) and 0.11 to 2.94 mg/kg manganese (Mn). The relationship between tree height, tree spread and tree volume with soil N, P, K, Zn, Cu and Mn were found to be positive and significant. A negative but significant relationships of soil N was found with duration of flowering while as the total numbers of flowers and male flowers had highly significant correlation with N, P, Zn, Cu and Mn. The relationship of soil N, P, Cu and Mn were found to be highly significant with fruit weight, length, diameter, volume, pulp weight, dry weight of pulp, stone weight and pulp: stone ratio, while as negative significant correlation of specific gravity with P, Fe and Cu. Soil N, P, K, Mg, Zn, Fe, Cu and Mn exhibited positive and significant relationship with TSS, TSS: acid ratio, total sugar, reducing sugar, ash content, pH, protein, polyphenols and starch.
A field experiment was conducted at Instructional-cum-Research Farm of Assam Agricultural University, Jorhat, Assam, India during kharif season of 2018 and 2019 to study the effects of organic nutrient management on growth, yield attributes, yield and quality of aromatic rice varieties. The variation in the growth, yield attributes and yield among the aromatic rice varieties was found significant and the highest grain yield was recorded in Keteki joha which was found to be statistically at par with Chakhao poireiton. Among the organic nutrient management treatments, application of vermicompost @ 30 kg N/ha+ in situ green manuring with Sesbania aculeate+seedling root dip treatment with Azospirillium and phosphorus solubilizing bacteria (PSB) @ 3.5 kg /ha each (N3) produced the highest number of panicles /m2, longest and heaviest panicle and the highest grain and straw yield during both the years. The quality profile in terms of crude protein content and content of Fe, Zn, Mn were influenced by the organic nutrient management and found highest with the N3 treatment. The aromatic rice varieties showed significant variations in their quality parameters. The cultivar Kola joha was found superior in respect of Fe, Zn and Mn content whereas Keteki joha was found superior in respect of crude protein, Ca and Mg content, head rice recovery percentage and length and breadth ratio over others. The lowest amylose content (6.23%) was recorded in case of Chakhao poireiton.