An Agave Counting Methodology Based on Mathematical Morphology and Images Acquired through Unmanned Aerial Vehicles

Abstract

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.

Comparison Of Algorithms For Agave Detection On Unmanned Aerial Vehicle Images

“…Estimation of the agave area also improved with the deep learning algorithms; the estimation error was less than 40 m² when compared with the estimate obtained in the field (Table 1). The algorithm with the best overall precision and the lowest error in estimating agave area was OBIA, with an overall precision of 0.96, which is slightly better than that reported by Calvario et al (2020) andFlores et al (2021) for the detection of blue agave. The deep learning algorithms used in this study have the advantage that during the classification process they start from a distribution-free assumption; that is, no underlying model is assumed for the multivariate distribution of class-specific data in the feature space.…”

“…A main advantage of OBIA is that it has the ability to detect agaves of a variety of sizes and ages. The algorithm based on mathematical morphology also has this ability (Jean-Philippe et al 1994;Calvario et al, 2020).…”

“…In Mexico, the main strategy to restore wild and cultivated agave populations is assisted recovery, which consists of in vitro germination and subsequent transfer to restoration sites (Rangel-Landa et al, 2015). However, agave population parameters, including growth rate, recruitment, and presence of nurse plants, are mainly monitored manually, which involves considerable effort (Calvario et al, 2020).…”

“…UAVs can provide high resolution images (1-2 cm), depending on the height of the flight and the focal length of the camera (Torres-Sánchez et al, 2015). This has been very useful for counting and morphometric analysis of agaves; for example, Calvario et al (2020) conducted a blue agave count using images taken from a DJI Phantom 4 UAV, and applied an algorithm based on mathematical morphology. They found that the estimate of the number of plants identified automatically in the images had an overall precision between 0.830 and 0.980.…”

Plant detection and counting from high-resolution RGB images acquired from UAVs: comparison between deep-learning and handcrafted methods with application to maize, sugar beet, and sunflower

“…Most of them belong to the Object Based Image Analysis (Josue Nahun Leiva et al, 2017; Koh et al, 2019; Torres-Sánchez et al, 2015; Varela et al, 2018; Zhao et al, 2018). The identification process can be done based also on the expert knowledge (Gnädinger and Schmidhalter, 2017; Jacopin et al, 2021; T. Liu et al, 2016) or by calibrating a statistical model over a training dataset (Calvario et al, 2020). More recently, approaches based on deep-learning (DL) have been proposed.…”