Bhoomin Tanut’s research while affiliated with Kamphaeng Phet Rajabhat University and other places

This article develops a colorimetric equation and a colorimetric model to create a smartphone application that identifies the ripening stage of the lady finger banana (LFB) (Musa AA group ‘Kluai Khai’, กล้วยไข่ “gluay kai” in Thai). The mobile application photographs an LFB, automatically analyzes the color of the banana, and tells the user the number of days until the banana ripens and the number of days the banana will remain edible. The application is called the Automatic Banana Ripeness Indicator (ABRI, pronounced like “Aubrey”), and the rapid analysis that it provides is useful to anyone involved in the storage and distribution of bananas. The colorimetric equation interprets the skin color with the CIE L*a*b* color model in conjunction with the Pythagorean theorem. The colorimetric model has three parts. First, COCO-SSD object detection locates and identifies the banana in the image. Second, the Automatic Power-Law Transformation, developed here, adjusts the illumination to a standard derived from the average of a set of laboratory images. After removing the image background and converting the image to L*a*b*, the data are sent to the colorimetric equation to calculate the ripening stage. Results show that ABRI correctly detects a banana with 91.45% accuracy and the Automatic Power-Law Transformation correctly adjusts the image illumination with 95.72% accuracy. The colorimetric equation correctly identifies the ripening stage of all incoming images. ABRI is thus an accurate and robust tool that quickly, conveniently, and reliably provides the user with any LFB’s ripening stage and the remaining days for consumption.

In this paper, we introduce a new class of nonexpansive type of mapping namely, CAK-generalized nonexpansive mapping, which is more general than an AK-generalized nonexpansive mapping and $\alpha$-nonexpansive mapping. Then, we obtain the proposition of the approximation method for an CAK-generalized nonexpansive in Hilbert spaces.

This article presents a new model for forecasting the sugarcane yield that substantially reduces current rates of assessment errors, providing a more reliable pre-harvest assessment tool for sugarcane production. This model, called the Wondercane model, integrates various environmental data obtained from sugar mill surveys and government agencies with the analysis of aerial images of sugarcane fields obtained with drones. The drone images enable the calculation of the proportion of unusable sugarcane (the defect rate) in the field. Defective cane can result from adverse weather or other cultivation issues. The Wondercane model is developed on the principle of determining the yield not through data in regression form but rather through data in classification form. The Reverse Design method and the Similarity Relationship method are applied for feature extraction of the input factors and the target outputs. The model utilizes data mining to recognize and classify the dataset from the sugarcane field. Results show that the optimal performance of the model is achieved when: (1) the number of Input Factors is five, (2) the number of Target Outputs is 32, and (3) the Random Forest algorithm is used. The model recognized the 2019 training data with an accuracy of 98.21%, and then it correctly forecast the yield of the 2019 test data with an accuracy of 89.58% (10.42% error) when compared to the actual yield. The Wondercane model correctly forecast the harvest yield of a 2020 dataset with an accuracy of 98.69% (1.31% error). The Wondercane model is therefore an accurate and robust tool that can substantially reduce the issue of sugarcane yield estimate errors and provide the sugar industry with improved pre-harvest assessment of sugarcane yield.

This article presents a defect detection model of sugarcane plantation images. The objective is to assess the defect areas occurring in the sugarcane plantation before the harvesting seasons. The defect areas in the sugarcane are usually caused by storms and weeds. This defect detection algorithm uses high-resolution sugarcane plantations and image processing techniques. The algorithm for defect detection consists of four processes: (1) data collection, (2) image preprocessing, (3) defect detection model creation, and (4) application program creation. For feature extraction, the researchers used image segmentation and convolution filtering by 13 masks together with mean and standard deviation. The feature extraction methods generated 26 features. The K-nearest neighbors algorithm was selected to develop a model for the classification of the sugarcane areas. The color selection method was also chosen to detect defect areas. The results show that the model can recognize and classify the characteristics of the objects in sugarcane plantation images with an accuracy of 96.75%. After the comparison with the expert surveyor’s assessment, the accurate relevance obtained was 92.95%. Therefore, the proposed model can be used as a tool to calculate the percentage of defect areas and solve the problem of evaluating errors of yields in the future.