Agriculture in Trinidad and Tobago has not advanced at the same rate at which new agricultural technology has been released. This has led to large-scale abandonment of crop lands as challenges posed by labor availability and their agronomic capability could not meet the technological demands for agricultural production, competitiveness and sustainability. There is an urgent need to develop technology-based agriculture models to meet the demands of a modern agricultural sector and to maintain its lead role in food production. This project looks at the development of advanced precision agriculture techniques for crop production, nutrient and water management. The project will utilize a combination of optimization principles (financial, yields, etc), agronomic data, advanced processing of remotely sensed images of agricultural fields and Geographic Information Systems (GIS) technology for the analysis and integration of the spatio-temporal farming data. A decision support system will be built upon the crop spatio-temporal intelligence for effective and efficient farm operations.

In the first phase of the project, the primary goal is to evaluate the effect of precision agriculture based irrigation and fertilizer technologies on (1) the growth, yield and quality of a corn crop, and (2) the soil nitrogen (N). In this presentation, we describe the replicated split plot Randomized Block Design (RBD) experimental design comprising of four irrigation (I) methods (Main factor) and three N fertilizer (F) rates (sub plot factor). This design will facilitate the assessment of the individual effects of irrigation (I) and N fertilization (F), as well as any I x F interaction effects, on the respective soil and plant parameters monitored through the project. The selection criteria for the Unmanned Aerial Vehicle (UAV) fitted with a Near Infra Red (NIR) Camera to collect multispectral data across the crop field are also discussed. By analyzing the “big data” comprising of the spectral reflectance values of the NIR, Red and Green bands, we will categorize problematic areas within the field. Based on this analysis, zones will be identified for irrigation and N management interventions.