Agricultural and Biological Engineering

Soil salinization risk assessments under future climate conditions: The case of central Huai Luang River Basin, Northeast, Thailand

2024-10-16T10:47:50+07:00

Salt-affected soil is one of the main problems decreasing the productivity of agriculture in Northeast Thailand. The Central Huai Luang Basin is the important rice producing areas of Udon Thani Province that is affected by saline soil. Regional and local groundwater flow systems are the major mechanisms responsible for spreading saline groundwater, waterlogging and the consequences of saline soils in this basin. Climate change may have an impact on groundwater recharge, on water table depth and the consequences of waterlogging, and on the distribution of soil salinity in this basin. A simulation model, in combination with groundwater models and the Geographical Information System (GISs), could be used to evaluate the risk of salinization. Five data, including soil salinity, soil group, irrigation area, groundwater salinity and waterlogging were used to input criteria data into a soil salinity risk model, which then calibrates the soil salinity simulation using field data. Three future climate conditions of RCPs from the CanESM2 models were downscaled to investigate the impact of future climate conditions on soil salinity risk. The impact of climate change was investigated by using a set of groundwater numerical models, namely HELP3 and SEAWAT, to estimate groundwater recharge and flow, and salt transport of the groundwater simulation, respectively. The results revealed that within the next 30 years (2045) the future average annual temperature and precipitation are projected to increase by 1.79^oC and 7.56% from current figures, respectively. The results showed that the impact of climate change on soil salinity distribution is projected to increase in every climate condition. By 2045 the salinity risk area will increase by about 216.90 km², or 23.62% from the current salt-affected area. The projected soil salinity assessment presented here is useful for targeting critical areas that may require special management for preventing or controlling soil salinization.

Research and development of strawberry quality sorting machine with image processing

2024-10-25T11:04:03+07:00

The objective of this research was to research and develop a strawberry quality sorting machine with image processing. The machine size is 1.60 x 4.0 x 1.5 meters. The prototype sorting strawberry fruit sizes into 5 grades according to the fruit grade standards of the Royal Project Foundation and sorting misshapen fruits, bruised, over ripe. This research wrote an image processing program using LabVIEW version 2018, taking pictures of strawberries while sorting, uses a webcam with a resolution of 640 x 320 pixels. The mechanism for sorting strawberries into baskets uses air blowing. The sorting system is automatically operated by PLC programmed control. The test was done at 0.08, 0.10 and 0.13 m s-1 of belt conveyor speed, the result showed the good condition for strawberry grading at 0.08 m s-1. The average of sorting accuracy was 93.78 %, average working capacity was 3,273 fruits hour-1, comparing with manual grading it is 2.17 times faster. Sorted strawberries by machine and manual labor showed no damage to the fruit. The machine cost is 250,000 baht, the operation time is 7 years and the break-even point is 1.94 years.

Suitable vegetation indices for predicting sugarcane Brix content in the field

2024-10-25T11:09:29+07:00

The goal of this research was to search for the optimal vegetation indices to predict the Brix content of sugarcane. The sugarcane of Khon Kaen 3 at age of 10 to 13 months was investigated. The models were optimized using regression equation by generating the relationship between four vegetation index (i.e NDVI GNDVI PVR and RVI) taken from multispectral image and measured °Brix content from ground sampling. The calibration model was developed using the sugarcane sample obtained from Sugarcane Breeding Institute, Khon Kaen university, while validation sets were the sugarcane sample from Bahn Phai Distric, Khon Kaen Province, and Saraburi province. The result showed that the overall °Brix content ranged between 16.8 and 24.8 ^oBrix. The vegetation index of GNDVI gave the best performance to be used as calibration model, providing the linear relationship of Y = -22.482x+31.832, where Y is predicted °Brix, x is GNDVI index. For the validation results, sugarcane in Bahn Phai Distric, Khon Kaen Province had the Bias value of 0.40 ^oBrix, and SEP value of 1.64^oBrix, meanwhile Saraburi province plant provided the Bias value of 2.80 ^oBrix, and SEP (standard error of prediction) value of 0.80 ^oBrix. The result indicated that multispectral imaging across the GNDVI index could be used as a real monitoring of cane quality in the field.

Development of a Jerusalem artichoke washing machine

2024-12-01T19:55:38+07:00

The objectives of this research were to design and performance assessment of a Jerusalem artichoke washing machine. The expectation of this research was to raise production of Jerusalem artichoke to higher performance and safe work-times due to the shortage of labor in the agricultural sector. Due to the decline in agricultural labor caused by migration to urban areas, the use of machinery can compensate for the labor shortage and enable the remaining farmers to continue their operations. The components of Jerusalem artichoke washing machine are basin, barrel washing, water purifier, and water pump. Performance was evaluated from capacity, percentage of cleaning, percentage of damage and electric power. Three levels of barrel speed (17, 27 and 37 rpm) and two levels of time (5 and 10 min). The result showed that barrel speed significantly impacts all indicators. Time significantly impacts only electric power. The barrel speed was 27 rpm with time about 5-10 min were suitable operating factors of the Jerusalem artichoke washing machine, providing capacity, percentage of cleaning, percentage of damage and electric power about 4.93 kg/h, 57.20%, 9.47%, and 6.93 kwh, respectively.