Accuracy of Various Methods to Estimate Volume and Weight of Symmetrical and Non-Symmetrical Fruits using Computer Vision

Many researchers have used images to measure the volume and weight of fruits so that the measurement can be done remotely and non-contact. There are various methods for fruit volume estimation based on images, i.e., Basic Shape, Solid of Revolution, Conical Frustum, and Regression. The weight estimation generally uses Regression. This study analyzed the accuracy of these methods. Tests were done by taking images of symmetrical fruits (represented by tangerines) and non-symmetrical fruits (represented by strawberries). The images were processed using segmentation in saturation color space to get binary images. The Regression method used Diameter, Projection Area, and Perimeter as features that were extracted from the binary images. For symmetrical fruits, the best accuracy was obtained with the Linear Regression based on Diameter (LDD), which gave the highest R2 (0.96 for volume and 0.93 for weight) and the lowest RMSE (5.7 mm3 for volume and 5.3 gram for volume). For non-symmetrical fruits, the highest accuracy for non-symmetric fruits was given by the Linear Regression based on Diameter (LRD) and Linear Regression based on Area (LRA) with an R2 of 0.8 for volume and weight. The RMSE for LRD and LRA for strawberries was 3.3 mm3 for volume and 1.4 grams for weight

Automatic Estimation of Human Weight From Body Silhouette Using Multiple Linear Regression

Estimating weight based on 2D image is advantageous especially for contactless and rapid measurement. Several researches used additional thermal camera or Kinect camera, required subjects to do front and side pose and manually extract body measures. This research propose an algorithm to estimate body weight automatically using 2D visual image where subject only do front pose. This research studied 4 features of body measures which are: (F1) height, and width of (F2) shoulder, (F3) abdomen/waist plus arm, (F4) feet. Each feature was simply subtracted based on body proportion where normal body has 8 equal segments. Shoulder is in 2nd segment, abdomen/waist is in 4th segment and feet is in the last segment. Multiple Linear Regression is used to determine weight estimation formula of all combination of 4 features, 15 in total. The highest significance R2 (0.80) and RMSE 2.68 Kg is given when using all 4 features in the estimation formula

Face Detection of Thermal Images in Various Standing Body-Pose using Facial Geometry

 Automatic face detection in frontal view for thermal images is a primary task in a health system e.g. febrile identification or security system e.g. intruder recognition. In a daily state, the scanned person does not always stay in frontal face view. This paper develops an algorithm to identify a frontal face in various standing body-pose. The algorithm used an image processing method where first it segmented face based on human skin’s temperature. Some exposed non-face body parts could also get included in the segmentation result, hence discriminant features of a face were applied. The shape features were based on the characteristic of a frontal face, which are: (1) Size of a face, (2) facial Golden Ratio, and (3) Shape of a face is oval. The algorithm was tested on various standing body-pose that rotate 360° towards 2 meters and 4 meters camera-to-object distance. The accuracy of the algorithm on face detection in a manageable environment is 95.8%. It detected face whether the person was wearing glasses or not

Deteksi Gulma Berdasarkan Warna HSV dan Fitur Bentuk Menggunakan Jaringan Syaraf Tiruan

Gulma merupakan tanaman pengganggu dalam lahan pertanian. Herbisida merupakan obat yang efektif membunuh gulma tersebut. Penyemprotan herbisida harus tepat sasaran kepada gulma saja dan tidak mengenai tanaman. Penelitian ini membuat sistem yang dapat mendeteksi gulma secara otomatis di antara tanaman pada lahan pertanian riil. Sistem ini menggunakan gambar lahan pertanian riil dimana tanaman tampak utuh (daun dapat lebih dari satu) yang diambil menggunakan kamera dengan posisi vertikal menghadap ke bawah. Algoritma yang dibuat menggunakan segmentasi berdasarkan warna hijau dalam ruang warna HSV untuk mendeteksi daun, baik gulma maupun tanaman pada beragam pencahayaan. Sebanyak tiga fitur bentuk domain spasial digunakan untuk membedakan gulma dengan tanaman yang memiliki karakteristik bentuk daun yang berbeda. Fitur bentuk yang digunakan adalah Rectangularity, Edge-to-Center distances function, dan Distance Transform function. Klasifikasi gulma dan tanaman menggunakan metode Jaringan syaraf tiruan (JST) yang dapat dilatih secara offline. Dari 149 tanaman yang terdeteksi dimana 70% sebagai data training, 15% data validasi dan 15% data uji, didapati akurasi pengujian sebesar 95.46%.AbstractWeed is a major challenge in a crop plantation. A herbicide is the most effective substance to kill this unwanted vegetation. Spraying the herbicide must be done carefully to target the weeds only. Here in this research, we develop an algorithm that detects weeds among the plants based on the shape of their leaves. The detection is based on images that were acquired using a camera. The leaves of weeds and plants were detected based on their green color using segmentation in HSV color-space as it is more effective to detect objects in various illumination. Three shape features were extracted, which are Rectangularity that is based on Rectangularity, Edge-to-Center distance function, and Distance Transform function. Those features were fed into a learning algorithm, Artificial Neural Network (ANN), to classify whether it is the plant or the weed. The testing on the weed classification in a real outdoor environment showed 95.46% accuracy using a total of 149 detected plants (70% as training data, 15%  as validation data, and 15% as testing data)

Controlling the Nutrition Water Level in the Non-Circulating Hydroponics based on the Top Projected Canopy Area

Deep Water Culture Hydroponics is suitable for a large-scale plantation as it does not require turn-on the electric pump constantly. Nevertheless, this method needs an electric aerator to give Oxygen to the roots. Kratky’s and Dry Hydroponics are the two methods that suggest an air gap between the raft and the nutrient water level. The gap gives Oxygen to the roots without an aeration pump. Controlling the nutrient water level is required to give a good distance of air gap for Precision Agriculture. The root length estimation used to be done manually by opening the raft, but this research promotes automatic and non-contact estimation using the camera. The images are used to predict the root length based on the Top Projected Canopy Area (TPCA) using various Regression Methods. The test shows that the TPCA gives a high correlation toward the Root Length (>0.9). To control the nutrient water level, this research compares If-Else and the Linear Regression. The error between the actual level that is measured using an Ultrasonic sensor and the setpoint is fed to an Arduino Uno to control the duration of an inlet pump and the outlet pump. The If-Else and the Linear Regression method show good results

Inner-Canthus Localization of Thermal Images in Face-View Invariant

Inner-canthus localization has played an essential role in measuring human body temperature. This is due to the theory that human core body temperature can be measured in the inner-canthus. Such measurement is useful for mass screening since it is non-contact, non-invasive and fast. This paper presents an algorithm that has been developed to locate the inner-canthus. The algorithm proposed a robust method in various face-view, i.e., frontal, sided and tilted. The algorithm consisted of: face segmentation, determining face-orientation, rotating face into straight view, eye localization, and inner-canthus localization. The face segmentation used human temperature threshold of 34°C — the face orientation used trend line of a middle point between each most-bottom and most-top coordinates. The face rotation was based on the gradient of the trend line. Once the face is rotated, the eye location was determined using facial proportion. The inner-canthus location was determined as the highest intensities in the eye-frame. The test on 15 thermal images of faces with various view showed localization accuracy of 80% for eye-frame determination and 100% for inner-canthus localization

Classification of Physical Soil Condition for Plants using Nearest Neighbor Algorithm with Dimensionality Reduction of Color and Moisture Information

Determining the quality of soil is an important task to perform especially on newly opened agricultural land since it may provide significant impact on the growth of plants. One alternative to determine physical soil quality is by visually observe the color of the soil and measure its moisture. This paper designed an embedded system classify soil condition for plants according to the dimensionality reduction of color and moisture information from the soil using k-NN algorithm. The dimension of attribute information was reduced using correlation analysis to achieve lower computational time and lower memory usage on embedded system. In this study, 39 sample of soil from various location were collected and categorized by soil expert using visual observation. In the accuracy testing on the system that used 4 attributes, 100% accuracy was given by 60:40 ratio with 7 neighbors. In contrast, the system that used only 2 attributes, 100% accuracy was given by 60:40 ratio with 5 nearest neighbors. The resource usage testing shown that by using reduced attributes dimension, the resource usage can be lowered as many as 188 bytes on program storage and 192 bytes on global variable usage. Moreover, the average of computation time performed by the system using reduced attribute dimension achieved 5.4 ms compared to the system that used all attributes which achieved 6.2 ms

Sistem Object Tracking pada Quadcopter Menggunakan Segmentasi Citra dengan Deteksi Warna HSV dan Metode Regresi Linier Berbasis Raspberry Pi

Saat ini, banyak aplikasi perangkat cerdas yang dikembangkan untuk melakukan tugas secara mandiri tanpa menerima perintah dari manusia. Oleh karena itu, mengembangkan sistem yang memungkinkan perangkat untuk melakukan tugas pengawasan seperti mendeteksi dan melacak objek bergerak akan memungkinkan tugas yang lebih canggih untuk diterapkan pada robot di masa depan. Teknologi Quadcopter sesungguhnya dapat memudahkan pekerjaan manusia dalam melakukan pengawasan dan pelacakan seperti pada kasus pelacakan lansia atau ABK (Anak Berkebutuhan Khusus) secara otomatis agar kerabat dapat melakukan pengawasan dengan menggunakan drone. Sehingga penelitian ini dilakukan untuk membuat sebuah sistem pada drone atau quadcopter agar dapat mendeteksi objek dan mengikutinya. Pada implementasinya, orang yang berkebutuhan khusus dan membutuhkan pengawasan akan mengenakan atribut berupa topi dengan warna solid. Warna topi tersebut akan dijadikan acuan untuk threshold segmentasi warna untuk mendeteksi objek topi tersebut dengan pemrosesan citra digital. Pergerakan drone ditentukan oleh prediksi jarak, sudut, dan ketinggian objek berdasarkan regresi linier yang dihasilkan dari 123 data latih. Hasil deteksi sistem juga cukup sesuai dengan pergerakan drone ketika diuji dengan 27 data. Akurasi dari prediksi gerak pitch adalah 84%, prediksi gerak yaw adalah 94%, dan prediksi gerak up/down adalah 91,5%. Adapun waktu komputasinya adalah 0.175829662 detik per frame. Abstract Nowadays, many intelligent device applications are developed to perform tasks independently without receiving commands from humans. Therefore, developing systems that allow devices to perform surveillance tasks such as detecting and tracking moving objects will allow more sophisticated tasks to be applied to robots in the future. Quadcopter technology can actually facilitate human work in monitoring and tracking, such as in the case of tracking the elderly or children with special needs automatically so that relatives can carry out surveillance using drones. So this research was planned to create a system on a drone so it can detect objects and follow them. In its implementation, people with special needs and need supervision will wear an attribute in the form of a hat with a solid color. The color of the hat will be used as references for the color segmentation threshold to detect the hat object with digital image processing. The movement of the drone is determined by the prediction of the distance, angle, and height of the object based on linear regression generated from 123 training data. The system detection results are also quite in accordance with the movement of the drone when tested with 27 data. The accuracy of pitch motion prediction is 84%, yaw motion prediction is 94%, and up/down motion prediction is 91.5%. The computation time is 0.175829662 seconds per frame

Sistem Deteksi Jumlah, Jenis dan Kecepatan Kendaraan Menggunakan Analisa Blob Berbasis Raspberry Pi

Penghitungan kondisi lalu lintas guna analisa kualitas jalan raya umumnya dilakukan secara manual. Hal ini tentunya membutuhkan biaya dan SDM yang tinggi serta tidak dapat dianalisa secara langsung. Dalam penelitian ini telah dikembangkan metode pengenalan jenis, jumlah dan kecepatan kendaraan secara otomatis menggunakan pengolahan citra digital. Metode yang berdasarkan analisa terhadap BLOB (Binary Large OBject) tersebut ditanamkan pada sistem berbasis Raspberry Pi. Setiap blob merupakan connected-component yang diperoleh dari proses thresholding terhadap perubahan nilai pixel dari sebuah frame dan frame rujukan dalam metode background subtraction. Jenis kendaraan ditentukan oleh jumlah piksel dalam bounding-box setiap blob. Jumlah kendaraan yang melaju dihitung dengan  memberikan garis virtual dimana jumlahnya akan bertambah jika centroid dari setiap bounding-box kendaraan melewatinya. Kecepatan kendaraan dihitung dengan membagi jarak sebenarnya dari koordinat awal hingga garis virtual sepanjang 12 meter yang dibagi dengan waktu centroid tersebut untuk menempuhnya. Algoritma tersebut diimplementasikan pada sistem berbasis Raspberry Pi dengan input kamera yang terhubung dengan serial monitor untuk menampilkan output penghitungan. Pengujian akurasi deteksi jenis kendaraan yakni sepeda motor, kendaraan ringan dan berat menghasilkan akurasi 93,39%. Pengujian jumlah kendaraan menghasilkan rata-rata akurasi 93,48% untuk semua jenis kendaraan. Pengujian laju kendaraan yang dideteksi dengan dibandingkan kecepatan pada spedometer kendaraan menunjukkan akurasi 93,9%. AbstractAn analysis on traffic condition usually carried out manually by visual observation. This method demands high human resource and cannot be analysed immediately. This paper present an algorithm to analyse type, number and speed of vehicles that passing by a road automatically using BLOB (Binary Large Object)  analysis. Each blob is a connected-component as a result of thresholding after background subtration process. Type of vehicles was determined by measuring pixel number of blob’s bounding box. Number of vehicles was determined by drawing virtual line where the number was increased once a centroid of bounding box passed it. Speed of vehicles was determined using basic speed formula where 12 meters of actual distance between the beginning coordinate and virtual line was divided by time to travel it. The algorithm was embedded in Raspberry Pi where videos were acquired using attached web camera. The analysis result was shown in connected serial monitor. Testing on vehicles’ type detection (motorcycle, light vehicle, heavy vehicle) result accuracy of 93.9%, number of vehicles result accuracy of 93.48%, whilst speed of vehicles result accuracy of 93.9%

Automatic Measurement of Human Body Temperature on Thermal Image Using Knowledge-Based Criteria

Instead of thermometer, an infrared camera could be uti- lized to scan body temperature instantly and non-contact. This paper proposed a non-contact measurement of human body temperature by au- tomatically locating inner-chantus on thermal images. The inner-canthus were detected in both eyes individually. It located inner-canthi based on temperature where inner-canthi has the highest temperature in face area. A Thresholding based on 9-highest temperature were applied to detect candidates of inner-canthus' blob as it must have minimum 9 pixel area according to the Standard. Three knowledge based on characteristic of eye were also applied in the algorithm as several spot in face usually falls within the temperature threshold. The result show accuracy of al- gorithm to detect eye is 82% whether the eyelids were open or closed. There is no signicant dierent of temperature between closed and open eyes based on paired t-test. The algorithm also showed similar result to thermometer measurement based on paired t-test