## 4 Conclusions  
The use of drone and satellite imagery can bring great benefits in the agricultural domain, in applications such as water management, fertigation, monitoring, and harvesting decisions. In this study, a complete computer vision system has been proposed to recognize the different ripening stages of apples of the Red Delicious variety under natural outdoor conditions. The final purpose of the system is to enable an accurate irrigation strategy based on the current maturity state of the crop, as well as to determine the proper time to recollect the fruits from the trees.  
The proposed computer vision system has three main parts: segmenting the apples in the video frames; extracting color features of the apples; and classifying them in a ripening class using an ANN. The first step is based on the combination of four basic methods and has shown a very high accuracy in detecting 98.4% of the apples. A reduced set of the six most effective color features are computed  
Figure 5: A sample video frame after application of the proposed apple ripening classification. The number corresponds to the ripening label (1. unripe; 2. half-ripe). A sample video with more results is available at [[https://youtu.be/A3](https://youtu.be/A3) ROtqRy9 os]([https://youtu.be/A3](https://youtu.be/A3) ROtqRy9 os).  
for each segmented object; these features indicate the importance of the red channel in the problem of interest, but always in relative terms to the other channels. Finally, classification is done with an ANN that has been optimally configured with a genetic algorithm. The obtained values of sensitivity, specificity, and accuracy for the four classes are always above 96% over the test set, achieving a global accuracy of 97.88%. These facts suggest that the system can be potentially applied under real-world industrial applications. The measure of the computational efficiency indicates that the process could be applied very fast in an average general-purpose processor. The adaptation to other types of apples or fruits should not require a change in the process--only the application of the most effective feature selection and the training of the hybrid ANN-GA classifier with the new data of the problem.  
Several interesting future research lines arise from this work. First, defining the optimal irrigation strategy using the overall maturity state of the fruits would be necessary for a complete automation of water management. Second, it could be possible to predict the fruit production of the crop adding a counting and averaging algorithm able to solve the problems of occlusion and overlapping. Moreover, the size of the apples could also be estimated accurately by image processing after solving both issues.  
Conceptualization, S.S. and Y.A.-G.; Methodology, S.S., Y.A.-G., J.I.A., and G.G.-M.; Software, S.S.; Validation, S.S., Y.A.-G., J.I.A., and G.G.-M.; Formal Analysis, S.S., Y.A.-G., and J.I.A.; Investigation, S.S., Y.A.-G., J.I.A., G.G.-M., A.R.-C., and J.M.M.-M.; Resources, S.S. and Y.A.-G.; Writing--Original Draft Preparation, S.S. and J.I.A.; Writing--Review & Editing, J.I.A., G.G.-M., A.R.-C., and J.M.M.-M.; Supervision, Y.A.-G.; Project Administration, Y.A.-G.; Funding Acquisition, Y.A.-G., G.G.-M., A.R.-C., and J.M.M.-M.  
This research was funded by the Iran National Science Foundation (INSF) through the research project 96007466 and by the Spanish MINECO, as well as European Commission FEDER funds, under grant numbers TIN2015-66972-C5-3-R and AGL2015-66938-C2-1-R.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.