Approximation of fruit ripening quality index for IoT based assistive e-healthcare

Abstract

This research was carried out by using 100 Apples to check whether low-cost ripening could be measured. The preselected apples were first grouped as per their ripening index/percentage. Next, two specially designed aluminum probes were attached with a microcontroller board to perform the analog read operation, from test-apples. This analog read value resembled to the unknown resistance $$\left( {R_{n} } \right)$$ value of test-apples. Results obtained from this experiment was validated against a pre-calibrated indexing table with consultation with a professional horticulturist that undoubtedly got matched with the analog readings. It was also found that higher values were related to lower probe-distance and vice versa. For example, under 1 cm probe-distance following values were found: 143.5 ± 0.33, 151.8 ± 0.59, 162.25 ± 0.63, 163.04 ± 0.34, and 169.37 ± 0.27 for Apple 1–5 categories, respectively. The inferred fact was as follows: Apple 5 category (169.37 ± 0.27) was the most ripen ones whereas Apple 1 category (143.5 ± 0.33) was found to be greenest of all. The collected sensor readings were then sent to the Internet of Things (IoT) based cloud storage platform i.e. Carriots for storing the values permanently and further processing. Thus, the proposed novel experiment can be successfully modified per requirement of fruit-based food processing industries. Moreover, the same could be useful for the personalized and ubiquitous e-healthcare in daily life to check whether the given fruit is consumable.