advancing precision agronomy for minimizing production risk
abstract
farming in northwestern ontario faces unique challenges, including a shorter growing season,
severe weather conditions, and limited infrastructure and support services. despite these obstacles,
the region holds great potential for expanding agricultural production, particularly for crops like
soybeans. soybean, a crop of significant economic and nutritional value, is susceptible to pests,
diseases, and environmental stresses that reduce productivity. effective health monitoring is
crucial to optimize yields and quality. this study explored the use of low-cost proximal field
cameras and remote sensing techniques for monitoring soybean leaf chlorophyll. a mapir
survey3w camera was selected to capture high spatial resolution images in the green, red, and
near-infrared regions of the electromagnetic spectrum. the optimal camera setup was investigated
by comparing vertical (90º) and oblique (45º) orientation angles and automating image capture
using a raspberry pi 4 model b powered by a solar panel system. the vertical camera showed
higher spectral reflectance values, while no significant difference was detected for vegetation
indices. once a series of images were captured using the identified optimal camera configurations,
the images were preprocessed to obtain spectral reflectance values. vegetation indices, such as the
green normalized difference vegetation index (gndvi), were calculated from the captured
images over the growing season. for calibration and validation purposes, at each field visit (within
7-10 days time), soybean leaf chlorophyll content (lcc) was measured using apogee instruments
mc-100 chlorophyll meter. the correlation between gndvi and lcc was established over time
using the inverse function of piecewise linear regressions. the robustness of the regression models
was measured by a kolmogorov–smirnov statistical comparison test between the predicted lcc
over time and the field-measured lcc. the results were statistically not significant, indicating the
similarity between the two data sets. finally, a user-friendly prototype software application was
built to make the proposed model accessible to the public. this study provided valuable insights
into the optimal setup of field cameras and the use of low-cost remote sensing techniques for
soybean leaf chlorophyll monitoring. the proposed methodologies and analyses contribute to the
remote sensing techniques in agriculture using affordable sensors, supporting sustainable
agriculture practices, and minimizing production risks in soybean cultivation.