Resumen de Multi-scale and multi-sensor remote sensing in international agricultural development
Personal food security means that they have physical and economic access to sufficient, safe, and quality food. On the other hand, there are three main causes of food insecurity: 1. the high vulnerability of agriculture to climate change; 2. local, national, or international conflicts; and 3. economic inequality. The United Nations (UN) Sustainable Development Goals (SDGs), which are objectives that should be attained by 2030, are targeted at both developing and developed nations. The second SDG, "Zero Hunger," aims to double small-scale food producers' productivity and earnings while promoting resilient agricultural methods and ensuring sustainable food production. Agriculture is heavily reliant on factors related to climate change conditions such as abiotic stress, which includes soil nutrient deficiencies, accelerating temperature rises, drought, and rising CO2 concentrations; and biotic stress, which includes invasive pests, disease outbreaks, and decreased crop output. Remote sensing (RS) technologies can provide several non-destructive methods for identifying and quantifying various types of stress. For the application of RS, it is relevant to consider the different types of resolution: spectral, spatial, temporal, and radiometric. Also, the different observation scales are ground- based, aerial, space-based, or using orbital satellites. In this thesis, we evaluated the practical implementation of non-destructive methods using RS technologies across the four chapters. Moreover, we compared the maturity levels between different types of technologies using Technological Readiness Level (TRL) assessments. In the first chapter, our objective was to estimate the grain yield of the maize under low nitrogen using Vegetation Indices (VIs) from RGB (Red, Green, Blue composite color images) sensors at the ground and aerial levels in Sub-Saharan Africa (SSA). We developed an RS system in the second chapter to monitor an early warning fall armyworm (FAW) across SSA. In the third one, using leaf spectral reflectance and advanced regression models, we estimated the Vc,max, and Jmax of soybean and peanut. And in the last chapter, we developed a user-friendly mobile app for the Middle East and North Africa (MENA) countries for plant disorders detection on tomatoes, cucumbers, peppers, and quinoa, covering everything from data collection to deep learning model creation, to web and mobile app launch. Different RS technologies were used in different countries at different scales and with different types of sensors. Nevertheless, it was very relevant to consider the objectives of each study because they determined the type of spectral, spatial, and temporal resolution and the scale of observation. Regarding the TRLs across the four chapters, they suggest that the level of technology readiness depends on the goals, the time to develop the project, the amount of data collection required, and the robustness of the validation.
