Development of a robotic structure for acquisition and classification of images (ERACI) in sugarcane crops

Abstract

Digital agriculture contributes to agricultural efficiency through the use of such tools as computer vision, robotics, and precision agriculture. In this study, the objective was to develop a system capable of classifying images through the recognition of pre-established patterns. For this purpose, a geographically distributed system was created, based on the Raspberry Pi 3B+ computer, which captures images in the field and stores them in a database, where they are available to receive a pre-classification by a supervisor. Subsequently, classifiers are generated, evaluated, and sent to the remote device to conduct a classification in real time. For an evaluation of the system, 23 classes were defined and grouped into 3 superclasses, 36,979 images were captured, and 1,579 pre-classifications were conducted, which allowed the classification tests to be carried out by means of a cross-validation by randomly dividing into the equivalent number of classes. These tests revealed that the accuracy delivered by each classifier is different and directly proportional to the quantity and balance of the samples, with a variation of 11% to 79%, with 26 and 2,200 samples considered, respectively. The response time of the system was evaluated during 1,585 periods and was maintained within approximately 0.20 s, and under controlled speed of the vehicle, can be used for the dispersion of inputs in real time.