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.
WOS Indexed (2026)
30 Jun 2026 (Vol - 57 , Issue- 07 )
31 Jul 2026 (Vol - 57 , Issue 07 )
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
Standardization and development of functional foods from mango seed kernels
Food processing industry by products or food wastes is produced in large amounts in the food processing industries annually around the world. The plant based food processing industries such as fruits and vegetable processing, cereals and pulse processing, nuts and oil seed processing industries etc., mainly produce by products such as bran, husk, pomace, seed, peel, shell, seeds, stems, seed coat during processing. They are dumped as waste or utilized as cattle feed and land filling of these by products cause environment pollution and loss of valuable nutrient components. Food processing industry by products give a promising source of bioactive and functional compounds which may be utilized because of their favorable nutritional and therapeutic properties. Demand for novel functional foods is rising rapidly owing to the increasing health awareness among consumers. The functional foods are used to reduce health risks (Cardiovascular disease, cancer, osteoporosis, obesity, diabetes and metabolic disease, musculoskeletal disease) and improve health quality and health maintenance India is the second major producer of fruits and vegetables in the world. It contributes 10 % of world fruit production. Fruit wastes are rich in antioxidants and phyto-chemicals. Thus fruit processing wastes are useful in serving the functional properties. The aim of the study is to develop a sustaining and functional food products based on the by – products generated from the mango processing industries in Tamil Nadu. This research helps to use better economic utilization of mango processing industries. All these developed functional products have regular and expanding market both in India and foreign countries. This will pave a way to promote entrepreneurship in the area of fruit processing industries.
Evaluation of Production Potential and Feasibility of Different Forage Based Cropping System for Round the year Fodder Production
In the view of sustainable livestock production ample delivery of quality forage is very essential. A field experiment was conducted during 2012-13 to 2016-17 at Research Farm of Bihar Agricultural College; Sabour to identify the suitable forage based cropping system for quality fodder production to get sustainable agriculture production in round the year. The experiments is comprising of seven treatment in randomized block design (RBD), replicated thrice. The detail of all treatments were T1 (NB hybrid + Cowpea – Barseem - Lobia), T2 (Guinea grass + Cowpea - Barseem – Summer Bajra), T3 (Guinea grass + M. Sorghum- Barseem- Ricebean), T4 (Multicut Sorghum - Barseem – Maize + Cowpea), T5 (Sorghum- Barseem- Maize + Cowpea), T6 (Maize + Cowpea – Oat - Summer Bajra + Rice bean) and T7 (Sorghum + Cowpea – Oat – Summer Bajra + Rice bean). However in the cropping system the nutrient was supplied to different crop component on the basis of recommended dose of fertilizer as per treatment. The five years results revealed that Multicut Sorghum– Barseem - Maize + Cowpea cropping system under the treatment (T4) produced significantly higher Green fodder and dry fodder yield e. i. 1412 and 324.89 q/ha with higher net return (Rs. 2,29485) and benefit cost ratio (3.27) over the other treatments. The maximum average crude protein content was found in Napier Hybrid + Cowpea – Barseem– Lobia/cowpea cropping system (17.12%) which was significantly higher than that under all other treatments. Similarly, the maximum total crude protein yield was found in Multicut Sorghum – Barseem – Maize + Cowpea cropping system (25.78 q ha-1) followed by Sorghum – Barseem – Maize + Cowpea and Guinea grass + Cowpea – Barseem – Summer Bajra cropping systems e. i. 20.53 and 18.08 q ha-1 respectively. Inclusion of perennial grasses with annual forage provides continuous supply of green fodder round the year.
Isolation, screening and biochemical characterization of cellulase producing microorganisms
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.
A novel identification method of bollworm in apple orchard based on MC-Mask R-CNN
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.
EFFECT OF POLLINATION METHODS ON FRUITING OF DIFFERENT STRAWBERRY CULTIVARS
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.