Crop Stress Management using Multi-source Data Fusion

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Precision Agriculture (PA) rationalizes farm inputs and mitigates the adverse impact of agricultural practices on the environment while increasing farm productivity and profitability. PA requires accurate detection, classification, quantification and mapping of biotic (weeds, pests) and abiotic (water, nutrient deficiency, chemicals, heat) stresses as the primary piece of information in site-specific stress management. Soil type, water content, surface topography and remote sensing satellite data complement this information for deploying effective stress control. This research program is designed to improve overall decision support systems in PA through application of Computer Vision (CV) and Machine Learning (ML) techniques for site-specific stress management. There are several open challenges with CV and ML-based applications in PA such as (1) the scarcity of publicly available image datasets representing real field conditions for stress management; (2) extremely difficult and tedious preprocessing of such data; (3) methods based on single type (primarily images) data for target problems; (4) unaddressed occlusion and overlap problem; and (5) lack of holistic approach for stress management. The long-term objective of this research program is to apply CV and ML techniques to develop technology solutions for the agriculture sector that are cost-effective, sustainable, environment friendly, and socially acceptable. The short-term objectives, which will address the aforementioned gaps over the next five years are to (1) develop a semi-automated image labelling tool for agricultural applications, (2) create novel field vegetation indices based on multi-source image data for improved temporal monitoring of crop development, (3) identify, classify, quantify, and monitor biotic and abiotic stresses by applying ML techniques on multisource data like high-resolution imagery, soil data, moisture data and weather data (4) develop a decision support system for canola crop management by using ML techniques to detect, quantify and dynamically threshold foliation damage caused by flea beetle. This research program will provide the basis for making informed decisions and will help in economical, environment-friendly, and socially viable agricultural practices. This will have a positive impact on the Canadian economy and world food supply. Four highly qualified personnel will be trained in the areas of ML, CV, and PA. The research program is aligned with the federal innovation agenda on artificial intelligence (Pan-Canadian Artificial Intelligence Strategy) and the strategic research plan of the University of Regina (digital future cluster and ground security initiative).