## 1 Introduction  
Non-destructive estimation of fruit ripeness in orchards is one of the most important challenges for agronomical engineering researchers worldwide, since many management decisions can be directly related with the maturity state of fruits. Establishing a relationship between maturity and the visual appearance of fruits--such as color and texture--allows for the development of new irrigation, harvesting, and post-harvesting strategies based on crop growth analysis as well as new yield prediction methods [1]. Different imaging systems in the field of non-destructive analysis of ripeness have already been applied by different authors, including hyperspectral, multi-spectral, and visible light [2; 3], the last one being the cheapest. However, the images are usually high resolution and there is very little research in estimating the state of fruit ripening using low-resolution images, as those obtained with satellites or drones. For example, in [4], a system is presented to detect fruits in a crop of pomegranates to monitor its growth and estimate the yield, using quadcopters. Some researchers have studied the changes of physical and chemical properties of different fruits during ripening [5; 6; 7; 8]. For example, in [9], visible and near-infrared spectral images were used to determine the physicochemical properties and evaluate the internal quality of two nectarine varieties. Sixty hyperspectral pictures were taken at wavelengths between 450 and 1040 nm. This information was correlated with the physicochemical properties of the nectarines, by means of least partial squares regression, showing a coefficient of determination of 0.87. Another investigation was conducted in [10] about the ripening of Golden Delicious apples during storage. The apples were harvested green and stored in a chamber under controlled conditions. A total of 114 apples were studied over 40 days. To evaluate the color changes in apple skin, the process used the three channels of the L*a*b* color space; this is a uniform color space suitable for industrial applications which consists of a luminance channel, L*, and two chrominance channels, a* and b*. Results showed that L* increased slightly from Day 1 to Day 16, remained constant from Day 16 to Day 24, and decreased from Day 32 to Day 40. Ultraviolet (UV) light has been used too as a method to measure the chlorophyll content of fruits, which is linked with the ripening of different types of climacter fruits [11]. For example, in [12], a small low-cost portable spectrometer connected to a smartphone is presented; this device is used to measure the amount of chlorophyll in the skin of apples of different varieties using UV; these measures are correlated with destructive firmness tests on the fruits. The process of fruit maturity has also been related to changes in their dielectric properties. In [13], the ripening of red banana is analyzed using features such as capacitance, relative permittivity and impedance, and image processing techniques such as local binary patterns (LBPs). The classifiers showed a high accuracy in predicting the different ripening stages. It has also been shown that chemical properties of the fruits are related to maturity [14]. In this research, caroten and chlorophyll levels at different ripening stages of tomato were studied using spectroscopic imaging. It was observed that there is a correlation between them, concluding that it is possible to predict these two characteristics without destructive measurements. In [15], gas sensors installed in drones are shown to be another feasible way to estimate the maturity of apples for optimal harvesting decisions, in particular by detecting ethylene emission. Other interesting applications of image analysis in apple crops include tasks, such as automatic pruning of the trees based on branch detection and 3D reconstruction [16]. On the other hand, regarding irrigation strategies, neural network techniques were applied on satellite images to manage a central pivot irrigation system in Colorado (USA) [17].