The use of Unmanned Aerial Vehicles (UAVs) based on remote sensing has generated low cost monitoring, since the data can be acquired quickly and easily. This paper reports the experience related to agave crop analysis with a low cost UAV. The data were processed by traditional photogrammetric flow and data extraction techniques were applied to extract new layers and separate the agave plants from weeds and other elements of the environment. Our proposal combines elements of photogrammetry, computer vision, data mining, geomatics and computer science. This fusion leads to very interesting results in agave control. This paper aims to demonstrate the potential of UAV monitoring in agave crops and the importance of information processing with reliable data flow.

Blue agave is an important commercial crop in Mexico, and it is the main source of the traditional mexican beverage known as tequila. The variety of blue agave crop known as Tequilana Weber is a crucial element for tequila agribusiness and the agricultural economy in Mexico. The number of agave plants in the field is one of the main parameters for estimating production of tequila. In this manuscript, we describe a mathematical morphology-based algorithm that addresses the agave automatic counting task. The proposed methodology was applied to a set of real images collected using an Unmanned Aerial Vehicle equipped with a digital Red-Green-Blue (RGB) camera. The number of plants automatically identified in the collected images was compared to the number of plants counted by hand. Accuracy of the proposed algorithm depended on the size heterogeneity of plants in the field and illumination. Accuracy ranged from 0.8309 to 0.9806, and performance of the proposed algorithm was satisfactory.

While innovation is essential for sustainable development, micro, small, and medium-sized enterprises (MSMEs), which account for more than 90% of firms in Latin America, face the challenge of benefiting systematically from innovation due to capability and negotiation asymmetries when compared with large organizations. In this context, open innovation holds promise to enable shared-value creation in terms of developing MSME capabilities, operations, and the organization of activities, especially when mediated and supported by public sector actors. It may also hold promise for the development of MSMEs when there is a lack of well-developed ecosystems with multiple central actors, as is the case in many less-developed Latin American countries, such as Nicaragua. Open innovation ecosystems support platforms that form the delivery vehicles for the offerings of firms, providing a framework of processes, rules, and policies for the purpose of co-creating value. These platforms also offer a development gateway for the participating MSMEs, impacting the achievement of the Sustainable Development Goals (SDGs) created by The United Nations. Despite the potential for open innovation and its application in entrepreneurship ecosystems, few cases document the essential elements for designing these supporting platforms. In this case study, we aim to provide a framework for mediated, shared-value open innovation platforms by applying design science and case study approaches. Our work contributes to the field of knowledge-based ecosystems and open innovation platforms and considers best practices that can be applied in similar contexts.

The responsible and sustainable agave-tequila production chain is fundamental for the social, environment and economic development of Mexico's agave regions. It is therefore relevant to develop new tools for large scale automatic agave region monitoring. In this work, we present an Agave tequilana Weber azul crop segmentation and maturity classification using very high resolution satellite imagery, which could be useful for this task. To achieve this, we solve real-world deep learning problems in the very specific context of agave crop segmentation such as lack of data, low quality labels, highly imbalanced data, and low model performance. The proposed strategies go beyond data augmentation and data transfer combining active learning and the creation of synthetic images with human supervision. As a result, the segmentation performance evaluated with Intersection over Union (IoU) value increased from 0.72 to 0.90 in the test set. We also propose a method for classifying agave crop maturity with 95% accuracy. With the resulting accurate models, agave production forecasting can be made available for large regions. In addition, some supply-demand problems such excessive supplies of agave or, deforestation, could be detected early.