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 (2025)
07 Dec 2025 (Vol - 56 , Issue- 12 )
31 Dec 2025 (Vol - 56 , Issue 12 )
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
Real-time Apple Quality Inspection on a Sorting Machine Using YOLOv7 and SORT
Apples, a popular fruit, require thorough quality inspections to enhance their commercial value. To assess apple quality in terms of the presence of defects, this study proposes a real-time apple sorting system that combines a single RGB camera with the YOLOv7 object detection model and the SORT tracking algorithm. Using images from the apple sorting machine, the system trains the YOLOv7 model to detect five defect types, stems, and calyxes while differentiating between defective and non-defective apples. The SORT algorithm integrates multi-frame data, tracks, and keeps apple labels. With YOLOv7 achieving 71.7% mAP and 69.4 fps in defect detection and SORT achieving 91.2% MOTA and 85.2% MOTP, the proposed system demonstrates high efficiency. Combining detection and tracking yields an overall quality rating of 95.8% F1 and 60.9 fps. However, defect-specific detection, such as insect damage (46.9% mAP) and physical damage (38.3% mAP), requires improvement. This system—effective for apple quality inspection, shows potential for broader agricultural product sorting applications.
Marketing Pattern and Marketing Efficiency of major spice in Meghalaya: A value chain Mapping
The study was conducted in Ribhoi district and West Jaintia Hills district of Meghalaya to examine the marketing costs, price spread, marketing efficiency and the major constraints in marketing of the major spice in Meghalaya. The marketable surplus was found to be more than the marketed surplus and it shows no distress sales. The majority of ginger producers (61.73%) utilized channel-I (Producer → Local Trader/collection centre → Traders), while raw turmeric was mainly sold through channel-II (Producer → Processor-cum-wholesaler-cum-Retailer (SHGs/Co-operatives Society) → Consumer). In sliced turmeric channel-I (60.71%) was the preferred channel and in the case of powder turmeric channel-I (Producer → Consumer) was the most preferred channel. The marketing efficiency for ginger was high in Channel-IV, whereas raw turmeric (Channel-II), sliced (Channel-III) and powered turmeric (Channel-I) showed the greatest marketing efficiency. The major constraints faced by producers in marketing major spices included price fluctuations and low prices, while intermediaries faced challenges due to scattered production patterns and climate variability, followed by a lack of human resources. The price fluctuations and low prices faced by producers can be addressed by implementing market intelligence systems and price stabilization measures and investing in infrastructure for storage and processing to reduce post-harvest losses can improve the overall value chain. It was observed that through the intervention of the Mission Lakadong scheme the turmeric farmers benefited. Encouraging investors to the establishment of the oleoresin plant to tap the potential of the high curcumin content of the Lakadong turmeric varieties is the call of the hour.
STUDIES ON EFFECT OF ORGANIC MANURES AND BIOFERTILIZERS ON QUALITY PARAMETERS OF TURMERIC (Curcuma longa L.) VARIETIES
The present investigation on “Studies on the effect of organic manures, biofertilizers on growth, yield and quality of Turmeric (Curcuma longa L.) varieties” was conducted at College of Horticulture, Rajendranagar, SKLTSHU, Mulugu during 2019-20. The experiment was laid out in Factorial Randomized Block Design (FRBD) with 2 replications and 18 treatments. The results revealed that among all the organic manures, biofertilizers, varieties and their combinations, treatment M1 – FYM 45 t/ha + AMC (Arka microbial consortium) 1 lit per quintal manure recorded maximum antioxidant activity (12.01 µg mL-1), Curcumin content (5.75 %) and oleoresin content (9.25 %) followed by M3 – Vermicompost 14 t ha-1 + AMC (Arka microbial consortium) 1 l q-1 compost (12.53 µg mL-1), (5.46 %) and (8.22 %) respectively. Among the varieties, Salem recorded significantly the highest values for antioxidant activity (10.68 µg mL-1), curcumin content (6.03 %) and oleoresin content (10.20 %) followed by followed by V2 – First best treatment of first experiment (Vertical split of mother rhizome) of Rajendra Sonia variety (12.62 µg mL-1), (4.72 %) and (7.48 %) respectively. Among the interaction effects between organic manures, biofertilizers and varieties, the treatment M1V1 – FYM 45 t/ha + AMC (Arka microbial consortium) 1 lit per quintal manure + vertical split of mother rhizome of Salem variety recorded maximum antioxidant activity (10.18 µg mL-1) Curcumin content (7.14 %) and oleoresin content (12.38 %) followed by M3V1 – Vermicompost 14 t ha-1 + AMC (Arka microbial consortium) 1 l q-1 manure + First best variety of first experiment (Vertical split of mother rhizome) of Salem variety (10.51 µg mL-1), (6.41 %) and (10.69 %) respectively.
Response of fodder cowpea (Vigna unguiculata) to domestic sewage water irrigation
Though farmers` feed various vegetative biomass such as fresh, dry or preserved form for feeding livestock, the productivity was found low compared to the world average. As, fodder crops are found as a negligible crop, due importance is not given for cultivation of fodder crops in the field. Along, with this water is also found to be an important factor for not cultivating fodder crops. Fodder cowpea is one of the important fodder crops and fodder cowpea is deprived of resources mostly during cultivation due to its shorter duration and nitrogen fixing ability. As domestic sewage water contains nutrients required for plant growth and development, the experiment was an attempt to understand the response of fodder cowpea to realise its productivity and its effect on soil fertility. A pot experiment with fodder cowpea was laid out in complete randomized block design during rabi season, 2019 and 2020 to assess the effect of different sources of irrigation water on the growth, productivity, nutrient uptake of fodder cowpea and soil fertility after harvest of fodder cowpea. The irrigation treatments consist of application of ground water, domestic sewage water, ground water and domestic sewage water alternatively. Investigation resulted in higher growth, productivity and nutrient uptake of fodder cowpea and improvement in post-harvest soil available nutrients with application of domestic sewage water.
Monitoring of Winter Rice and Acreage Estimation Using Sentinel-1 SAR Data in the Ribhoi District of Meghalaya
Assessing the acreage for crop classification using optical satellite data encounters difficulties in India's rainy season because of continuous cloud cover. Microwave SAR data offers a potential alternative solution, and this research employed an efficient blend of multi-date Sentinel-1 SAR data. During the winter rice season of 2022-23, six sets of SAR data from different dates between June and August were examined in the Ribhoi district of Meghalaya. VH polarization was chosen for classification due to its greater consistency and reliability compared to VV polarization. Supervised machine learning classification algorithms, particularly support vector machine, were implemented for classification using ArcGIS software. The accuracy of this technique was assessed by comparing it with ground truth data, yielding an overall accuracy of 87% with a kappa coefficient of 0.73. Additionally, the estimated winter rice area from SAR images closely matched the reported area (9327 ha) from the State Department of Agriculture under rice cultivation in Ri-bhoi district, confirming result consistency.