Management of Plant Growth Regulators in Cotton Using Active Crop Canopy Sensors

Abstract

Abstract:Factors affecting cotton development present spatial and temporal variability. Plant growth regulators (PGR) are used to control vegetative growth, promote higher yields, better fiber quality, and facilitate mechanical harvest. The optimal rate of PGR application depends on crop height, biomass, and growth rate. Thus, the objective of this study was to evaluate optical and ultrasonic crop canopy sensors to detect the crop spatial variability in cotton fields, and to develop strategies for using this information to perform variable rate application (VRA) of PGR in cotton. Field trials were conducted in Midwest Brazil during the 2013/2014 and 2014/2015 crop seasons. Two optical and two ultrasonic active crop canopy sensors were evaluated as tools to detect crop variability. On-farm trials were used to develop and validate algorithms for VRA based on within-field variations in crop response to PGR applications. The overall performance of the sensors to predict crop height and the accumulation of biomass in cotton was satisfactory. Short distance variability was predominant in some fields, reducing the performance of the sensors while making current technology for variable rate application of PGR inadequate. In areas with large scale variability, the VRA led to 17% savings in PGR products and no significant effect on yield was observed. Ultrasonic sensors present can be a low-cost alternative to implement variable rate application of PGR in real time.

How digital is agriculture in a subset of countries from South America? Adoption and limitations

“…2018; Schwalbert et al . 2020), and the precision agriculture lab at the University of Sao Paulo (Gimenez and Molin 2004, 2018; Trevisan et al . 2018; Molin et al .…”

“…Brazil have developed DA programs related to precision agriculture. Since 2000, the Aquarius Project at Federal University of Santa Maria (Amado et al 2016;Corassa et al 2018;Schwalbert et al 2020), and the precision agriculture lab at the University of Sao Paulo Molin 2004, 2018;Trevisan et al 2018;Molin et al 2020;Tavares et al 2021) develops research, innovation and extension on precision farming. The Brazilian national agricultural research organisation EMBRAPA have also contributed to the development of DA (Bolfe et al 2020;EMBRAPA 2020).…”

Use of Active Sensors in Coffee Cultivation for Monitoring Crop Yield

“…It is evident that AOS sensors have great potential to be explored in coffee farming; the present study sought to provide an initial approach regarding the potential of using such sensors for PA management. Future studies may address the use of sensors in other scenarios (e.g., crops with different row orientations, mountain coffee, shaded coffee), as well as exploiting this information to create management strategies that optimize the management of inputs, as was done for annual crops [3,13,19,21]. The spatio-temporal evaluation of coffee yield also showed that, in a single field, there may exist coffee plants with inverted bienniality (Figures 7 and 8).…”

“…This allows the synchronization of collection, diagnosis, prescription and application in one single step, making real-time interventions possible [3]. Studies were already developed using AOS for crops, such as maize [4][5][6][7], wheat [8][9][10][11], rice [12], cotton [13] and sugarcane [14][15][16]. However, these approaches were not explored in depth for coffee cultivation, with only a few scientific studies evaluating the performance of AOS to estimate biometric parameters of individual plants [17,18] and are, therefore, not applicable to large-scale contexts, such as in commercial areas.…”

Orbital, aerial, and proximal sensing applied to monitoring the spatial variability of coffee plantations

“…It is evident that AOS sensors have a great potential to be explored in coffee farming, and that the present study sought to provide an initial approach regarding the potential of using these sensors for PA management. Future studies may address the use in other scenarios (e.g., crops with different row orientation, mountain coffee, shaded coffee), as well as exploiting this information to create management strategies that optimize the management of inputs, as has been done for annual crops TREVISAN et al, 2018;PALLOTTINO et al, 2019;YU et al, 2021).…”