An embedded real-time red peach detection system based on an OV7670 camera, ARM Cortex-M4 processor and 3D Look-Up Tables

This work proposes the development of an embedded real-time fruit detection system for future automatic fruit harvesting. The proposed embedded system is based on an ARM Cortex-M4 (STM32F407VGT6) processor and an Omnivision OV7670 color camera. The future goal of this embedded vision system will be to control a robotized arm to automatically select and pick some fruit directly from the tree. The complete embedded system has been designed to be placed directly in the gripper tool of the future robotized harvesting arm. The embedded system will be able to perform real-time fruit detection and tracking by using a three-dimensional look-up-table (LUT) defined in the RGB color space and optimized for fruit picking. Additionally, two different methodologies for creating optimized 3D LUTs based on existing linear color models and fruit histograms were implemented in this work and compared for the case of red peaches. The resulting system is able to acquire general and zoomed orchard images and to update the relative tracking information of a red peach in the tree ten times per second

Sensors in Agriculture and Forestry

Agriculture and Forestry are two broad and promising areas demanding technological solutions with the aim of increasing production or accurate inventories for sustainability while the environmental impact is minimized by reducing the application of agro-chemicals and increasing the use of environmental friendly agronomical practices. In addition, the immediate consequence of this “trend” is the reduction of production costs

A proposal for automatic fruit harvesting by combining a low cost stereovision camera and a robotic arm

This paper proposes the development of an automatic fruit harvesting system by combining a low cost stereovision camera and a robotic arm placed in the gripper tool. The stereovision camera is used to estimate the size, distance and position of the fruits whereas the robotic arm is used to mechanically pickup the fruits. The low cost stereovision system has been tested in laboratory conditions with a reference small object, an apple and a pear at 10 different intermediate distances from the camera. The average distance error was from 4% to 5%, and the average diameter error was up to 30% in the case of a small object and in a range from 2% to 6% in the case of a pear and an apple. The stereovision system has been attached to the gripper tool in order to obtain relative distance, orientation and size of the fruit. The harvesting stage requires the initial fruit location, the computation of the inverse kinematics of the robotic arm in order to place the gripper tool in front of the fruit, and a final pickup approach by iteratively adjusting the vertical and horizontal position of the gripper tool in a closed visual loop. The complete system has been tested in controlled laboratory conditions with uniform illumination applied to the fruits. As a future work, this system will be tested and improved in conventional outdoor farming conditions

Implementasi dan Uji Kinerja Algoritma Background Subtraction pada ESP32

Salah satu hal penting pada computer vision adalah ciri (feature) citra. Ciri digunakan sebagai dasar untuk mendeteksi objek, baik itu benda, manusia maupun hewan. Ciri citra yang biasa digunakan dalam penelitian antara lain tepian, sudut, bentuk maupun gradient histogram. Penelitian ini menjelaskan kinerja algoritma background subtraction pada unit pemroses berdaya rendah sebagai salah satu algoritma pada computer vision. Algoritma ini memiliki kompleksitas yang rendah dan dapat digunakan untuk mendeteksi objek sehingga berpotensi diterapkan pada kamera keamanan. Algoritma ini bekerja dengan melakukan pengurangan nilai piksel current frame dengan background model. Penelitian ini telah berhasil menerapkan algoritma dasar pengolahan citra, yaitu algoritma background subtraction pada modul ESP32. Pengujian menggunakan input citra yang memiliki dimensi 80x60 piksel dengan format warna 8bit grayscale. Ukuran frame citra 80 x 60 piksel dipilih sebagai citra uji karena keterbatasan memory DRAM EPS32 sebesar 328 KB (kilobyte). Implementasi pada modul ESP32 yang dilengkapi dengan mikroprosesor Xtensa 32-bit LX6 yang bekerja pada frekuensi 240MHz dapat memproses algoritma background subtraction 10000 kali dalam waktu ±2000ms menggunakan input citra uji tersebut. Kata Kunci – Background Subtraction; ESP32; Image Processing; Microcontroller; Object Detection.Salah satu hal penting pada computer vision adalah ciri (feature) citra. Ciri digunakan sebagai dasar untuk mendeteksi objek, baik itu benda, manusia maupun hewan. Ciri citra yang biasa digunakan dalam penelitian antara lain tepian, sudut, bentuk maupun gradient histogram. Penelitian ini menjelaskan kinerja algoritma background subtraction pada unit pemroses berdaya rendah sebagai salah satu algoritma pada computer vision. Algoritma ini memiliki kompleksitas yang rendah dan dapat digunakan untuk mendeteksi objek sehingga berpotensi diterapkan pada kamera keamanan. Algoritma ini bekerja dengan melakukan pengurangan nilai piksel current frame dengan background model. Penelitian ini telah berhasil menerapkan algoritma dasar pengolahan citra, yaitu algoritma background subtraction pada modul ESP32. Pengujian menggunakan input citra yang memiliki dimensi 80x60 piksel dengan format warna 8bit grayscale. Ukuran frame citra 80 x 60 piksel dipilih sebagai citra uji karena keterbatasan memory DRAM EPS32 sebesar 328 KB (kilobyte). Implementasi pada modul ESP32 yang dilengkapi dengan mikroprosesor Xtensa 32-bit LX6 yang bekerja pada frekuensi 240MHz dapat memproses algoritma background subtraction 10000 kali dalam waktu ±2000ms menggunakan input citra uji tersebut. Kata Kunci – Background Subtraction; ESP32; Image Processing; Microcontroller; Object Detection