Development of a prototype robot and fast path-planning algorithm for static laser weeding

To demonstrate the feasibility and improve the implementation of laser weeding, a prototype robot was built and equipped with machine vision and gimbal mounted laser pointers. The robot consisted of a mobile platform modified from a small commercial quad bike, a camera to detect the crop and weeds and two steerable gimbals controlling the laser pointers. Visible-one laser pointers were used to simulate the powerful laser trajectories. A colour segmentation algorithm was utilised to extract plants from the soil background; size estimation was used to differentiate crop from weeds; and an erosion and dilation algorithm was developed to separate objects that were touching. Conversely, another algorithm, which utilised shape descriptors, was able to distinguish plant species in non-touching status regardless of area difference. Next, in order to reduce route length and run time, a new path-planning algorithm for static weeding was proposed and tested. It was demonstrated to be more efficient especially when addressing a higher density of weeds. A model was then established to determine the optimal segmentation size, based on the route length for treatment. It was found that the segmentation algorithm has the potential to be widely used in fast path-planning for the travelling-salesman problem. Finally, performance tests in the indoor environments showed that the weeding mean positional error was 1.97 mm, with a 0.88 mm standard deviation. Another test indicated that with a laser traversal speed of 30 mm/s and a dwell time of 0.64 s per weed, it had a hit rate of 97%

Using the parcel shape index to determine arable land division types

This paper presents a new index for determining the shape of land parcels. Parcel shapes are usually represented descriptively (i.e. ribbon-shaped, rectangular, irregularly shaped), which is useless for automated distinguishing between parcel shapes or for determining and distinguishing between the patterns formed by parcels. Thus, we developed a Parcel Shape Index (IOP) to describe parcel shape characteristics, and then tested it in the test area of Gorenje pri Divači to analyse selected fields – as irregular blocks, enclosures, continuous strips, and furlongs. We found that IOP allows for a differentiation of parcels according to their shape as well as parcel patterns formed due to the individual types of dividing arable land

Pengenalan Angka Tulisan Tangan Dengan Penerapan Freeman Chain Code yang Dimodifikasi

*Optical Character Recognition* (OCR) merupakan kemampuan komputer yang dapat mengenali dan mengubah karakter pada sebuah dokumen menjadi representasi simbolik digital. Salah satu bentuk dokumen yang dapat digunakan adalah formulir model C1 PPWP 2014. Pada PPWP 2014, rekapitulasi nasional memakan waktu yang cukup lama karena dilakukan secara manual. Dengan memanfaatkan OCR, perhitungan suara dapat dilakukan secara otomatis dengan melakukan pengenalan angka tulisan tangan pada formulir model C1 yang telah didigitasi sehingga proses rekapitulasi dapat diselesaikan dengan cepat. Namun permasalahannya adalah setiap orang memiliki cara penulisan angka yang berbeda, sehingga proses pengenalan angka tulisan tangan diharapkan dapat mengenali angka dengan baik. Sistem pengenalan angka tulisan tangan *offline* dibangun untuk mengenali karakter angka tulisan tangan pada formulir model C1. Formulir model C1 dikumpulkan dan dibentuk menjadi data set. Sistem terdiri dari beberapa tahap, tahap pertama yaitu tahap *preprocessing* untuk membentuk citra kerangka karakter angka tulisan tangan. Citra hasil olahan memasuki proses ekstraksi ciri menggunakan metode Freeman Chain Code yang dimodifikasi dengan melakukan pembagian region citra, pembentukan histogram chain code, dan penambahan 4 ciri visual. Selanjutnya hasil ekstraksi ciri diujikan pada dua buah metode klasifikasi yaitu K-NN dan JST *Backpropagation*. Pengujian pada sistem pengenalan angka tulisan tangan menggunakan metode ekstraksi ciri Freeman Chain Code yang dimodifikasi menunjukkan bahwa metode ini mampu mengenali karakter angka tulisan pada data set formulir model C1 dengan cukup baik. Dengan melakukan pembagian 9 region citra, normalisasi histogram chain code, dan penambahan 4 ciri, tingkat akurasi terbaik dapat mencapai 90.18% menggunakan K-NN dan mencapai 93.60% menggunakan JST *Backpropagation*

Efficient Algorithms for Large-Scale Image Analysis

This work develops highly efficient algorithms for analyzing large images. Applications include object-based change detection and screening. The algorithms are 10-100 times as fast as existing software, sometimes even outperforming FGPA/GPU hardware, because they are designed to suit the computer architecture. This thesis describes the implementation details and the underlying algorithm engineering methodology, so that both may also be applied to other applications