Correlations between the Growth Period and Fresh Weight of Seed Sprouts and Pixel Counts of Leaf Area

Abstract

Purpose: This study was carried out to predict the growth period and fresh weight of sprouts grown in a cultivator designed to grow sprouts under optimal conditions. Methods: The temperature, light intensity, and amount of irrigation were controlled, and images of seed sprouts were acquired to predict the days of growth and weight from pixel counts of leaf area. Broccoli, clover, and radish sprouts were selected, and each sprout was cultivated in a 90-mm-diameter Petri dish under the same cultivating conditions. An image of each sprout was taken every 24 hours from the 4th day, and the whole cultivating period was 6 days, including 3 days in the dark. Images were processed by histogram inspection, binary images, image erosion, image dilation, and the overlay image process. The RGB range and ratio of leaves were adjusted to calculate the pixel counts for leaf area. Results: The correlation coefficients between the pixel count of leaf area and the growth period of sprouts were 0.91, 0.98, and 0.97 for broccoli, clover, and radish, respectively. Further, the correlation coefficients between the pixel count of leaf area and fresh weight were 0.90 for broccoli, 0.87 for clover, and 0.95 for radish. Conclusions: On the basis of these results, we suggest that the simple image acquisition system and processing algorithm can feasibly estimate the growth period and fresh weight of seed sprouts.

Image Processing Methods for Measurement of Lettuce Fresh Weight

“…Harvesting plants at the optimal time can promote uniform quality production while preserving nutrients and reducing growing time. The optimal harvesting time for each crop can be determined based on the measurement of its fresh weight; this can be estimated using digital images of the crop (Macfarlane et al, 2007;Lati et al, 2011;Son et al, 2014;Easlon and Bloom, 2014). Such a machine vision system is one of the most advantageous sensing techniques for measuring crop-growing status, because it uses realtime sensing and does not cause crop damage.…”