Descriptor: A Comprehensive Multimodal Dataset for Analyzing Maturity and Quality Indicators in Radish (Raphanus sativus)

Ayan Sar, Tanupriya Choudhury, Tanmay Sarkar, Faisal Imran

Abstract: This work presents a novel multimodal dataset to address maturity and quality assessment in Radish (Raphanus sativus). The analyzed dataset includes 360 images taken from five samples of radish that were photographed at three points—morning, afternoon, and evening on 24 subsequent days. Since the imaging process was intended to capture the visual growth in the presence of natural light, it offered a temporal resolution to the dataset. Consistent photos were taken each time to track the changes in texture, shape, and color of each sample. Besides the image data, this dataset contains comprehensive quality assessment records, where each radish sample's texture, shape, color, and overall appearance were scored between 1 and 10. These ratings were done based on just observations and touch sensations in an attempt to quantify some of the physical characteristics of the radish as it grew to maturity. Specific data needs are documented in an Excel file that incorporates timestamps, sample I.D.s, and ratings for each parameter for easy reference and to facilitate examination. It creates a platform to examine the relationship between environmental conditions and radish growth phases to apply machine learning algorithms and computer vision to evaluate the maturity and qualities of agricultural crops. This dataset may be useful for increasing the forecast accuracy of precision farming and for a better understanding of the dynamics of radish development. IEEE SOCIETY/COUNCIL Computational Intelligence Society (CIS) DATA TYPE/LOCATION Images (.jpg); Malda, West Bengal, India DATA DOI/PID 10.17632/cwxdmk8zhz.1

External IDs:doi:10.1109/ieeedata.2024.3475330