Performance of Cambodia’s made vegetable transplanter for two-wheel tractors under tillage conditions

Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.

A new approach to measure spatial variability of soil parameters and field technique to test-value specific fertilizer

Information on the distribution of soil properties is important to know the status of nutrients in the soils based on which fertilizer nutrients are recommended. Given the variability of nutrients in the soils, making a site-specific fertilizer recommendation seems to be a compelling work. To determine the spatial variability of soil nutrients and to make judicious and precise fertilizer recommendations, new measures are designed with this study. These measures are tested against the soil samples (n = 43) for total nitrogen (N), organic matter (OM), phosphorus (P2O5), and potassium (K2O) in the study area. The descriptive statistical analysis indicated an average of low nitrogen and organic matter, while phosphorus was found to be very high and the level of potassium was high. The spread of nutrients across the data sets, however, included low, medium, high, and very high levels of ratings. The Deviation Square Index was developed and applied for the variability measurement and found that the largest variation was with phosphorus distribution, followed by potassium, nitrogen, and organic matter. The coefficient of variation (CV%) analysis also exhibited similar trends in nutrient distributions. Nitrogen was the main determinant explaining the variations in rice yield, while phosphorus and potash were negatively related to the yield. An index of fertilizer nutrient recommendation called Test-Value Specific Dose (TVSD) was developed and used to calculate the nutrient recommendation for each sampled location. This new method gave easy and more accurate doses of fertilizer over the blanket recommendation to fit the variations across the soil samples.

Performance of Cambodia’s made vegetable transplanter for two-wheel tractors under tillage conditions

Lyhour Hin, Lytour Lor, Sokleng Mang, Sokunthea San, Buntheang Oem, Sopheabunnarith Thy, Sokong Houth, Rei Loeurn, Sreytoch Sinh, Manuel R. Reyes

Article ID: 2839
Vol 5, Issue 3, 2024

DOI: https://doi.org/10.54517/ama.v5i3.2839
VIEWS - 2338 (Abstract)

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Abstract

In Cambodia, vegetable crops are planted by hand, making it hard to meet local market demands. However, this production can be boosted by using a mechanical transplanter with two-wheel tractors to cut input costs, when introduced to farmers, while production and productivity can be accelerated. Thus, this research aimed to (1) evaluate the working performance of a locally made vegetable transplanter against manual planting and (2) compare plant survival rates. The study included fabrication, testing, modification, and experiments with farmers, starting from January 2023 to July 2024. The transplanter was fabricated, tested, and improved by the Royal University of Agriculture. Then, two experiments were carried out with a vegetable farming community in Tram Kak District, Takeo Province, Cambodia. Tomato was selected for the testing, choosing seedlings aged four weeks. The randomized complete block design (RCBD) was applied for both experiments with two treatments, manual planting and transplanter use, replicated four times. The results show that the working performance of the transplanter was six times faster than manual planting. Its speed, total field capacity, and planting rate were 1.03 km/h, 0.052 ha/h, and 27 plants/min, respectively, but missed planting was about 4%. Within-row spacing was similar (0.58 m), while using the transplanter made the plants incline at a steeper angle (63°), but could save 81.9% of time, when compared to manual planting. Both treatments had 100% plant survival rates evaluated one week after the transplanting. In short, using the transplanter can save both time and labor, but further assessment should be made with more kinds of fruit vegetable based on different seedling ages, so that the specifications can be confirmed, which is good for actual adoption for farmers.

Keywords

cutting disk; plant survival rate; total field capacity; plant angle; working speed

References

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