Monitoring Chemical-Induced Ripening of Castor (Ricinus communis L.) by UAS-Based Remote Sensing

Castor is a crop with an evergreen habit so artificially-induced ripening is an essential precondition for mechanical harvesting of new dwarf annual hybrids. Plants' moisture imposes a determinant effect both on pre-harvest and post-harvest seed loss, so frequent monitoring of crop ripening is crucial for identifying the optimum moisture for harvest. Remote sensing information from Unmanned Aerial Systems (UASs) along with field measurements were utilized in the present study in order to evaluate three harvest aid chemicals, herbicides glyphosate (GLY) and diquat (DIQ) and the defoliant Spotlight(R) (DEF) for terminating the castor crop and identifying opportunities for using remote sensing as a tool for monitoring crop ripening. The results showed that glyphosate required more than two weeks to dry out the crop while diquat and spotlight(R) presented a rapid action within two to four days. Nineteen vegetation indexes (VIs) were derived from a multispectral and an RGB camera mounted on two UAS and were plotted against field measurements. NDVI presented a higher accuracy (R-2 = 0.67) for predicting the castor stems' and leaves' moisture content while OSAVI and SIPI2 were more powerful in predicting moisture of capsules (R-2 > 0.76). High efficiency was also obtained with VARI(green), an index estimated from the common bands of a conventional RGB camera. The best performing VIs were further utilized in multiple linear regression models also incorporating the date of spraying as information. The VI models further improved the predicting power with an R-2 of up to 0.73 for stems and leaves and 0.81 for capsules.

Automated summary

This study evaluated the use of Unmanned Aerial Systems and field measurements to assess the effectiveness of three harvest aid chemicals in terminating castor crops and monitoring crop ripening. The results showed that diquat and Spotlight(R) had rapid action, while glyphosate took longer. The normalized difference vegetation index (NDVI) proved to be accurate in predicting moisture content of stems and leaves, while OSAVI and SIPI2 were effective for predicting moisture of capsules.