Technological capabilities of agricultural units cannot be optimally used without extensive automation of production processes and the use of advanced computer control systems. (Research purpose) To develop an algorithm for recognizing the coordinates of the location and ripeness of garden strawberries in different lighting conditions and describe the technological process of its harvesting in field conditions using a robotic actuator mounted on a self-propelled platform. (Materials and methods) The authors have developed a self-propelled platform with an automatic actuator for harvesting garden strawberry, which includes an actuator with six degrees of freedom, a co-axial gripper, mg966r servos, a PCA9685 controller, a Logitech HD C270 computer vision camera, a single-board Raspberry Pi 3 Model B+ computer, VL53L0X laser sensors, a SZBK07 300W voltage regulator, a Hubsan X4 Pro H109S Li-polymer battery. (Results and discussion) Using the Python programming language 3.7.2, the authors have developed a control algorithm for the automatic actuator, including operations to determine the X and Y coordinates of berries, their degree of maturity, as well as to calculate the distance to berries. It has been found that the effectiveness of detecting berries, their area and boundaries with a camera and the OpenCV library at the illumination of 300 Lux reaches 94.6 percent’s. With an increase in the robotic platform speed to 1.5 kilometre per hour and at the illumination of 300 Lux, the average area of the recognized berries decreased by 9 percent’s to 95.1 square centimeter, at the illumination of 200 Lux, the area of recognized berries decreased by 17.8 percent’s to 88 square centimeter, and at the illumination of 100 Lux, the area of recognized berries decreased by 36.4 percent’s to 76 square centimeter as compared to the real area of berries. (Conclusions) The authors have provided rationale for the technological process and developed an algorithm for harvesting garden strawberry using a robotic actuator mounted on a self-propelled platform. It has been proved that lighting conditions have a significant impact on the determination of the area, boundaries and ripeness of berries using a computer vision camera.
Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition
