Agricultural and Biological Engineering

Development of suitable greenhouse to increase melon production efficiency

2024-07-29T20:19:24+07:00

This research aims to develop a greenhouse, which suitable for increasing melon production efficiency by developing ventilation of the new greenhouse to be better than the primitive greenhouse; semi-circular greenhouse (farmer’s greenhouse). The new greenhouse has overlap curve roof with overhead opening is designed for cooling down temperature in the greenhouse. Temperature and relative humidity in the greenhouse are controlled close to the outside by a control system and the automatic fan activated when the temperature reaches 40 °C. The greenhouse is, 30.0 m. long ,6 m. wide, 3.0 m. high of pole and 1.5 m high of roof. The structure is galvanized steel pipe. The roof is covered with plastic 150 microns thick, the 32-mesh net used for the side walls. The construction costs less than 100,000 THB per unit, exclude fertilizer, irrigation systems, automatic temperature and humidity control systems along with ventilation fan set. The temperature measurement in the new greenhouses shown that the temperature decreases 5 - 15 °C when the inside air is ventilated by fan system, moreover in a sunny day that temperature reaches 40 °C, the automatic fan system reduces temperature to 37 °C. The melon variety used in this experiment is the Morakot variety, which is a popular melon variety consumed in Thailand. In terms of productivity, it was found that the greenhouse had an average total yield of 512 kg, an average income of 26,327 THB and a net profit of 6,633.21 THB per production cycle. Meanwhile, the farmer's greenhouse has an average total yield of 476 kg, an average income of 24,720 THB and a net profit of 2,692.88 THB per production cycle. The sweetness of the melon form both types of greenhouses pass the standard. The greenhouse has an economic break-even point of 3.36 years with the lifetime of 10 years, while the farmer’s greenhouse has the break-even point of 3.21 years with the lifetime of 5 years.

Efficiency of weed with weeder machine in paddy field 3 rows

2024-08-19T21:39:54+07:00

The objective of this research is to develop a machine to eliminate weeds in rice fields. and test the performance of a 3-row, 2-wheel-drive weed-killing machine in rice fields with a HINOTA EA65A engine of 5.6 hp as the engine. Tested at speeds of 1.7 and 2.3 km/h. Use an average working speed of 2.1 km/h. It has a theoretical capacity of 1.31 rai/h. Actual working ability 0.91 rai/h. Work efficiency was 69.85%, efficiency in eliminating weeds the dry weight of weeds before extermination was 0.18 kg, the dry weight of weeds after extermination was 0.05 kg, the efficiency in eliminating weeds was 69%, and the fuel consumption was 3.86 l/rai. A comparative study of prototype weed exterminators in rice fields. With the developed weed-killing machine in black rice fields, it was found that the prototype weed-killing machine in black rice fields has an average weed-killing efficiency of 66%. It can be seen that the developed weed-killing machine for rice fields has a speed of work, theoretical ability, and actual ability to work higher than the prototype black rice weed exterminator, and the efficiency of eliminating weeds is higher as well.

Effect of feed rate and screw pressing speed to the performance of a charcoal block pressing machine

2024-09-30T20:46:57+07:00

This study aimed to examine the effects of feed rate and screw pressing speed on the performance of a cassava-stump charcoal block pressing machine, as well as to design and develop a prototype. The machine consists of several components, including a mixing tank, screw conveyor, hopper, screw pressing unit, transmission system, and main frame. The experiment tested feed rates of 80, 100, and 120 kg/h and screw pressing speeds of 80, 95, 110, and 125 rpm. Results showed that the produced charcoal blocks had an average length of 13.91±1.62 cm, an outer diameter of 4.12±0.05 cm, an internal diameter of 1.23±0.12 cm, and a moisture content of 3.31% (dry basis). When tested at a screw pressing speed of 125 rpm and a feed rate of 120 kg/h, with a mixture ratio of cassava stump charcoal, cassava starch, and water at 3.00:0.45:4.00 kg, the machine achieved a production capacity of 111.7 kg/h, specific energy consumption of 13.35 w-h/kg, a bulk density of 505.3 kg/m³, a charcoal strength ranging from 82.14 to 159.51 kN/m², and a heating value of 5113.3 cal/g.

A comparison to the vegetation index of KorKor 49 rice field from orthomosaic map and video by using unmanned aerial vehicle

2024-08-24T21:33:50+07:00

A comparison of the vegetation Index of KorKor 49 rice field from orthomosaic map and video by using unmanned aerial vehicle (UAV) aim to study a possibility of the RGB color index analysis for calculate the vegetation Index such as VARI, EXG, YIELD and Chlorophyll by using photos and videos from the UAV during the tillering, booting, flowering, and pre-harvesting stages. The RGB color index was analysis by 2 cases, Case 1 creating an orthomosaic map from photo (the photos were taken from UAV) by the process of photogrammetry using Agisoft Metashape program then analyzing the RGB color index by using QGIS program, Case 2 analysis the RGB color index by using video processing technique from MATLAB program. The result of the RGB color index from 2 case shown that it can be used to analysis the vegetation index, yield and chlorophyll which different situation. The comparison between 4 parameters shown that, VARI index the MATLAB analysis method has a trend to be consistent with rice growth than the QGIS method, EXG index the QGIS analysis method has a trend to be consistent with rice growth than the MATLAB method, chlorophyll value the MATLAB analysis method has a trend to be consistent with rice growth than the QGIS method and yield shown that, the QGIS analysis method can give precise a rice yield prediction than the MATLAB method which was error 14% in the pre-harvest stage.

Measuring damaged skin of mangosteen using image processing

2024-10-17T20:32:20+07:00

Mangosteen is a major economic crop. Currently, commercial production still faces challenges in terms of quality sorting, particularly in adhering to the skin color standards which serve as quality criteria. Presently, quality sorting heavily relies on the expertise of individuals, especially for mangosteen with damaged skin, which cannot be exported. Advances in image processing technology allow for quality sorting, thus this research aims to examine mangosteen with damaged skin using image processing techniques. A sample of 60 mangosteen fruits at six maturity levels, with 20 fruits per level, images were taken from four sides using RGB cameras, totaling 480 images. These images were analyzed and models were built for distinguishing between good skin and damaged-skinned mangosteen using Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Decision Tree (Fine Tree) algorithms. Results showed that all three algorithms performed similarly performance. For levels 1 through 6, the average accuracy rates were approximately 100, 95.61, 93.03, 99.63, 99.40 and 100, respectively, with average recall rates of 100, 96.60, 94.45, 99.90, 99.73, and 100, respectively. Analysis revealed that evaluating damaged skin at levels 2 and 3 had the lowest effectiveness, as the good skin colors of mangosteen at levels 2 and 3 closely resembled the colors of the damaged skin. Therefore, the research demonstrates that image processing can effectively separate damaged-skinned mangosteen from good-skinned.