Scopus Indexed
WOS Indexed (2022)
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.
01 Feb 2022 (Vol - 53 , Issue- 02 )
31 Jan 2022 (Vol - 53 , Issue 01 )
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:
Azerbaijan Medical Journal Gongcheng Kexue Yu Jishu/Advanced Engineering ScienceAgricultural 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
Ethnobotanical approaches of local plants of lodhran district, Punjab, Pakistan.
The present study is conducted to gather the native awareness of therapeutic plants of Lodhran District, south Punjab, Pakistan, and documentation of remarkable wild plants. The area hasten been studied before for his purpose. The area is rich in medicinal plans and local people are using I for various ailments. This study was donein 2019. The information was collected through specific questionnaires and field trips and local health care professionals including Hakeems, Daii, and Peer (Spiritual Healers) were interviewed. Documented plants were further identified by Flora of Pakistan. An overall of 66therapeutic plants with 36 families was acknowledged through various field trips and with detailed interviews of the specific people in the community and their local names and folk uses are described along with their dosage forms.
Detection of peanuts mildew based on hyperspectral image
Peanut mildew can produce aflatoxin with strong toxicity. A nondestructive and rapid detection method of peanut mildew based on hyperspectral technology was proposed. Firstly, 600 Dabaisha peanuts purchased in the market were selected, and 200 peanuts were randomly selected for mildew treatment, while the remaining 400 peanuts were kept aseptically. After 30 days, the spectral images of all peanut samples were collected by using the Zhuoli Huanguang Hyperspectral Instrument and the SpaceView was used for black and white correction. Then the ROI of each peanut spectral image was extracted with ENVI5.1 software and the Mean spectral reflection value of the region was calculated to obtain the sample spectral data, the reflection curves of moldy peanuts and healthy peanuts were observed, and it was found that there were significant differences between 500nm-600nm, so the mold visualization of moldy peanuts was carried out within this interval. In order to eliminate the influence of non-quality factor information in the hyperspectral data, three spectral preprocessing methods were adopted to eliminate the noise in the original spectral data. XGBoost, LightGBM and RandomForest algorithms were run to model the characteristic bands and detect the moldy peanuts. In all models, the accuracy of the algorithm could reach 100%, the similarity indexes all reached 1, and the loss was all 0. In terms of FIT_TIME, XGBOOST had the best performance, only requiring 0.042s, which was significantly different from other algorithms. The results showed that XGBOOST model was the most suitable algorithm for detecting peanut mold. This study provides a strong theoretical basis and technical support for the monitoring and identification of healthy and moldy peanuts in peanut processing industry.
Responses of the Antioxidative enzymes of few Sub1 rice (Oryza Sativa) genotypes to prolonged submergence stress in Odisha
The present experiment was conducted in Department of Plant physiology, OUAT, Bhubaneswar during kharif 2017 and kharif 2019 to screen out the NILs rice genotypes for submergence adaptation traits under coastal regions of Odisha. The potential involvement of activated oxygen species by submergence stress was studied in twenty rice genotypes thirteen sub1 NILs, two tolerant checks with six susceptible checks. These rice genotypes were subjected to 17 days of complete submergence. Under 17 days of complete submergence and after the submergence, the genotypes IR-85086-Sub 33 -3-2-1 and IR-88760-Sub 93-3-3 showed lower lipid Peroxidation in terms of malondialdehyde (MDA) level and also showed lower levels of ACC Oxidase activity (AAO) and presented higher activities of antioxidative enzymes, superoxide dismutase (SOD), catalase (CAT), Peroxidase (POX) when compared to the susceptible checks. The levels of SOD activity indicated that detoxification of O2 - to H2O2 was maintained at a stable level throughout the submergence stress until up to 17 days in tolerant genotypes. These findings suggested that tolerance to submergence stress in rice might be proven by increased the capacity of antioxidative system. In addition, SOD and CAT activity has much higher affinity for scavenging H2O2 than POX. The present study evaluated thirteen pairs of Sub1 near-isogenic lines (NILs) together with FR13A and other check genotypes in pot culture conditions to assess the survival and growth processes occurring during submergence and recovery that are associated with Sub1.
Experiments on the distribution of spray droplets in sand-pressed melon fields with different pesticide application equipments in Jingyuan County
In view of the mechanized plant protection wilt prevention and control problem, DJI 8 rotor UAV, 3WX-2000G stretcher sprayer 2 planting sand melon field spray test, comparison analyzed the influence of different application technology and tools on the droplet deposition distribution law, for the pest control requirements and application technology of plant protection tools. The test results show that the plant protection UAV pharmaceutical operation, due to the rotor airflow disturbance, the leaves in the sand melon, stretcher spray on the front of the blade has more droplet deposition, the back of the front deposition becomes less, thus increasing the effect of the wind field on the back of the blade. In terms of fog droplet density and coverage, there is no obvious difference in the UAV spray droplet density before and after the addition of additives, mainly because the crop leaves are hydrophilic, and in the hydrophilic crops, the addition of wet spreading agent when the spray has no obvious effect on its spray deposition effect. From the size of the fog droplets deposited on the water-sensitive paper, the stretcher sprayer on the front of the blade is significantly larger than that of the drone, while the difference on the back of the blade is smaller. The fog droplet density and coverage of plant protection UAV are less than the vehicle stretcher motorized sprayer, but the average droplet density on the front of the blade is 18 / cm2 and 14 / cm2, which is effective fog droplet density; adopt ST10001 nozzle, flight altitude of 3.5m and obtain optimal deposition distribution at flight speed of 3 m/s.
Response Characteristics of Hyperspectral Images for Determining the Moisture Contents of Potato (Solanum tuberosum) Tubers
The study investigated the hyperspectral reflectance responses to changes in the moisture content of potato tubers in a time series generated during oven drying. 17 chemometric preprocessing methods were used to eliminate the impact of spectrum noise on the spectral feature curve. The CatBoost, LightGBM, XGBoost, and other algorithms were used to obtain the effective feature spectra for hyperspectral images. Water content prediction models were derived by using selected feature spectra and the results indicated that the combined model based on the Lasso and XGBoost algorithms had the greatest prediction ability with the highest R2 value of 0.8908.