RESUMEN
BACKGROUND: Agriculture is one of the most crucial assets of any country, as it brings prosperity by alleviating poverty, food shortages, unemployment, and economic instability. The entire process of agriculture comprises many sectors, such as crop cultivation, water irrigation, the supply chain, and many more. During the cultivation process, the plant is exposed to many challenges, among which pesticide attacks and disease in the plant are the main threats. Diseases affect yield production, which affects the country's economy. Over the past decade, there have been significant advancements in agriculture; nevertheless, a substantial portion of crop yields continues to be compromised by diseases and pests. Early detection and prevention are crucial for successful crop management. METHODS: To address this, we propose a framework that utilizes state-of-the-art computer vision (CV) and artificial intelligence (AI) techniques, specifically deep learning (DL), for detecting healthy and unhealthy cotton plants. Our approach combines DL with feature extraction methods such as continuous wavelet transform (CWT) and fast Fourier transform (FFT). The detection process involved employing pre-trained models such as AlexNet, GoogLeNet, InceptionV3, and VGG-19. Implemented models performance was analysed based on metrics such as accuracy, precision, recall, F1-Score, and Confusion matrices. Moreover, the proposed framework employed ensemble learning framework which uses averaging method to fuse the classification score of individual DL model, thereby improving the overall classification accuracy. RESULTS: During the training process, the framework achieved better performance when features extracted from CWT were used as inputs to the DL model compared to features extracted from FFT. Among the learning models, GoogleNet obtained a remarkable accuracy of 93.4% and a notable F1-score of 0.953 when trained on features extracted by CWT in comparison to FFT-extracted features. It was closely followed by AlexNet and InceptionV3 with an accuracy of 93.4% and 91.8% respectively. To further improve the classification accuracy, ensemble learning framework achieved 98.4% on the features extracted from CWT as compared to feature extracted from FFT. CONCLUSION: The results show that the features extracted as scalograms more accurately detect each plant condition using DL models, facilitating the early detection of diseases in cotton plants. This early detection leads to better yield and profit which positively affects the economy.
RESUMEN
This dataset used the Digital Competency Scale (DCS) to describe Indonesian pre-service teachers' perceptions. The DCS instrument consisted of five constructs/dimensions, which are: 1) data and information literacy, 2) communication and collaboration, 3) digital content creation, 4) safety, and 5) problem-solving, with a total of 36 items using five-point agreement Likert scale. The data was gathered from 23 education and teacher training faculties at Muhammadiyah Universities in 14 provinces across Indonesia in the academic year 2021/2022. A total of 1400 students (18 to 23 years old) in their first to fifth years of study were recruited using the convenience sampling technique, where they participated in filling in the survey electronically using Google Form. The dataset was analysed with the Rasch model measurement approach using WINSTEPS version 5.2.3 software for data cleaning and validation, and reliability and validity testing of the instrument. This dataset analysis can help teacher-training institutions, or higher education policymakers design effective programmes to improve pre-service teachers' digital competencies. Furthermore, researchers can compare this dataset with more rigorous data from other countries.
