Automatic detection of crop rows in maize fields with high weeds pressure

This paper proposes a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to gyros, vibrations and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of three main processes: image segmentation, double thresholding, based on the Otsu’s method, and crop row detection. Image segmentation is based on the application of a vegetation index, the double thresholding achieves the separation between weeds and crops and the crop row detection applies least squares linear regression for line adjustment. Crop and weed separation becomes effective and the crop row detection can be favorably compared against the classical approach based on the Hough transform. Both gain effectiveness and accuracy thanks to the double thresholding that makes the main finding of the paper

Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera.

This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images

Automatic expert system based on images for accuracy crop row detection in maize fields

This paper proposes an automatic expert system for accuracy crop row detection in maize fields based on images acquired from a vision system. Different applications in maize, particularly those based on site specific treatments, require the identification of the crop rows. The vision system is designed with a defined geometry and installed onboard a mobile agricultural vehicle, i.e. submitted to vibrations, gyros or uncontrolled movements. Crop rows can be estimated by applying geometrical parameters under image perspective projection. Because of the above undesired effects, most often, the estimation results inaccurate as compared to the real crop rows. The proposed expert system exploits the human knowledge which is mapped into two modules based on image processing techniques. The first one is intended for separating green plants (crops and weeds) from the rest (soil, stones and others). The second one is based on the system geometry where the expected crop lines are mapped onto the image and then a correction is applied through the well-tested and robust Theil–Sen estimator in order to adjust them to the real ones. Its performance is favorably compared against the classical Pearson product–moment correlation coefficient

Estimation of Wheat Plant Density at Early Stages Using High Resolution Imagery

Crop density is a key agronomical trait used to manage wheat crops and estimate yield. Visual counting of plants in the field is currently the most common method used. However, it is tedious and time consuming. The main objective of this work is to develop a machine vision based method to automate the density survey of wheat at early stages. RGB images taken with a high resolution RGB camera are classified to identify the green pixels corresponding to the plants. Crop rows are extracted and the connected components (objects) are identified. A neural network is then trained to estimate the number of plants in the objects using the object features. The method was evaluated over three experiments showing contrasted conditions with sowing densities ranging from 100 to 600 seeds.m(-2). Results demonstrate that the density is accurately estimated with an average relative error of 12%. The pipeline developed here provides an efficient and accurate estimate of wheat plant density at early stages

A machine learning approach for digital image restoration

This paper illustrates the process of image restoration in the sense of detecting images within a scanned document such as a photo album or scrapbook. The primary use case of this research is to accelerate the cropping process for the employees of Cinetis, a company based in Martigny, Switzerland that specializes in the digitalization of old media formats. In this paper, we will first summarize the state of the art in this field of research. This will include explanations of various techniques and algorithms involved with feature and document detection used by various digital companies

A framework for autonomous mission and guidance control of unmanned aerial vehicles based on computer vision techniques

A computação visual é uma área do conhecimento que estuda o desenvolvimento de sistemas artificiais capazes de detectar e desenvolver a percepção do meio ambiente através de informações de imagem ou dados multidimensionais. A percepção visual e a manipulação são combinadas em sistemas robóticos através de duas etapas "olhar"e depois "movimentar-se", gerando um laço de controle de feedback visual. Neste contexto, existe um interesse crescimente no uso dessas técnicas em veículos aéreos não tripulados (VANTs), também conhecidos como drones. Essas técnicas são aplicadas para posicionar o drone em modo de vôo autônomo, ou para realizar a detecção de regiões para vigilância aérea ou pontos de interesse. Os sistemas de computação visual geralmente tomam três passos em sua operação, que são: aquisição de dados em forma numérica, processamento de dados e análise de dados. A etapa de aquisição de dados é geralmente realizada por câmeras e sensores de proximidade. Após a aquisição de dados, o computador embarcado realiza o processamento de dados executando algoritmos com técnicas de medição (variáveis, índice e coeficientes), detecção (padrões, objetos ou áreas) ou monitoramento (pessoas, veículos ou animais). Os dados processados são analisados e convertidos em comandos de decisão para o controle para o sistema robótico autônomo Visando realizar a integração dos sistemas de computação visual com as diferentes plataformas de VANTs, este trabalho propõe o desenvolvimento de um framework para controle de missão e guiamento de VANTs baseado em visão computacional. O framework é responsável por gerenciar, codificar, decodificar e interpretar comandos trocados entre as controladoras de voo e os algoritmos de computação visual. Como estudo de caso, foram desenvolvidos dois algoritmos destinados à aplicação em agricultura de precisão. O primeiro algoritmo realiza o cálculo de um coeficiente de reflectância visando a aplicação auto-regulada e eficiente de agroquímicos, e o segundo realiza a identificação das linhas de plantas para realizar o guiamento dos VANTs sobre a plantação. O desempenho do framework e dos algoritmos propostos foi avaliado e comparado com o estado da arte, obtendo resultados satisfatórios na implementação no hardware embarcado.Cumputer Vision is an area of knowledge that studies the development of artificial systems capable of detecting and developing the perception of the environment through image information or multidimensional data. Nowadays, vision systems are widely integrated into robotic systems. Visual perception and manipulation are combined in two steps "look" and then "move", generating a visual feedback control loop. In this context, there is a growing interest in using computer vision techniques in unmanned aerial vehicles (UAVs), also known as drones. These techniques are applied to position the drone in autonomous flight mode, or to perform the detection of regions for aerial surveillance or points of interest. Computer vision systems generally take three steps to the operation, which are: data acquisition in numerical form, data processing and data analysis. The data acquisition step is usually performed by cameras or proximity sensors. After data acquisition, the embedded computer performs data processing by performing algorithms with measurement techniques (variables, index and coefficients), detection (patterns, objects or area) or monitoring (people, vehicles or animals). The resulting processed data is analyzed and then converted into decision commands that serve as control inputs for the autonomous robotic system In order to integrate the visual computing systems with the different UAVs platforms, this work proposes the development of a framework for mission control and guidance of UAVs based on computer vision. The framework is responsible for managing, encoding, decoding, and interpreting commands exchanged between flight controllers and visual computing algorithms. As a case study, two algorithms were developed to provide autonomy to UAVs intended for application in precision agriculture. The first algorithm performs the calculation of a reflectance coefficient used to perform the punctual, self-regulated and efficient application of agrochemicals. The second algorithm performs the identification of crop lines to perform the guidance of the UAVs on the plantation. The performance of the proposed framework and proposed algorithms was evaluated and compared with the state of the art, obtaining satisfactory results in the implementation of embedded hardware

Proceedings of the 4th field robot event 2006, Stuttgart/Hohenheim, Germany, 23-24th June 2006

Zeer uitgebreid verslag van het 4e Fieldrobotevent, dat gehouden werd op 23 en 24 juni 2006 in Stuttgart/Hohenhei

Prediction of Early Vigor from Overhead Images of Carinata Plants

Breeding more resilient, higher yielding crops is an essential component of ensuring ongoing food security. Early season vigor is signi cantly correlated with yields and is often used as an early indicator of tness in breeding programs. Early vigor can be a useful indicator of the health and strength of plants with bene ts such as improved light interception, reduced surface evaporation, and increased biological yield. However, vigor is challenging to measure analytically and is often rated using subjective visual scoring. This traditional method of breeder scoring becomes cumbersome as the size of breeding programs increase. In this study, we used hand-held cameras tted on gimbals to capture images which were then used as the source for automated vigor scoring. We have employed a novel image metric, the extent of plant growth from the row centerline, as an indicator of vigor. Along with this feature, additional features were used for training a random forest model and a support vector machine, both of which were able to predict expert vigor ratings with an 88:9% and 88% accuracies respectively, providing the potential for more reliable, higher throughput vigor estimates

Detecção de linha de plantio de cana de açúcar a partir de imagens de VANT usando Segmentação Semântica e Transformada de Radon

In recent years, UAVs (Unmanned Aerial Vehicles) have become increasingly popular in the agricultural sector, promoting and enabling the application of aerial image monitoring in both scientific and business contexts. Images captured by UAVs are fundamental for precision farming practices, as they allow activities that deal with low and medium altitude images. After the effective sowing, the scenario of the planted area may change drastically over time due to the appearance of erosion, gaps, death and drying of part of the crop, animal interventions, etc. Thus, the process of detecting the crop rows is strongly important for planning the harvest, estimating the use of inputs, control of costs of production, plant stand counts, early correction of sowing failures, more-efficient watering, etc. In addition, the geolocation information of the detected lines allows the use of autonomous machinery and a better application of inputs, reducing financial costs and the aggression to the environment. In this work we address the problem of detection and segmentation of sugarcane crop lines using UAV imagery. First, we experimented an approach based on \ac{GA} associated with Otsu method to produce binarized images. Then, due to some reasons including the recent relevance of Semantic Segmentation in the literature, its levels of abstraction, and the non-feasible results of Otsu associated with \ac{GA}, we proposed a new approach based on \ac{SSN} divided in two steps. First, we use a Convolutional Neural Network (CNN) to automatically segment the images, classifying their regions as crop lines or as non-planted soil. Then, we use the Radon transform to reconstruct and improve the already segmented lines, making them more uniform or grouping fragments of lines and loose plants belonging to the same planting line. We compare our results with segmentation performed manually by experts and the results demonstrate the efficiency and feasibility of our approach to the proposed task.Dissertação (Mestrado)Nos últimos anos, os VANTs (Veículos Aéreos Não Tripulados) têm se tornado cada vez mais populares no setor agrícola, promovendo e possibilitando o monitoramento de imagens aéreas tanto no contexto científico, quanto no de negócios. Imagens capturadas por VANTs são fundamentais para práticas de agricultura de precisão, pois permitem a realização de atividades que lidam com imagens de baixa ou média altitude. O cenário da área plantada pode mudar drasticamente ao longo do tempo devido ao aparecimento de erosões, falhas de plantio, morte e ressecamento de parte da cultura, intervenções de animais, etc. Assim, o processo de detecção das linhas de plantio é de grande importância para o planejamento da colheita, controle de custos de produção, contagem de plantas, correção de falhas de semeadura, irrigação eficiente, entre outros. Além disso, a informação de geolocalização das linhas detectadas permite o uso de maquinários autônomos e um melhor planejamento de aplicação de insumos, reduzindo custos e a agressão ao meio ambiente. Neste trabalho, abordamos o problema de segmentação e detecção de linhas de plantio de cana-de-açúcar em imagens de VANTs. Primeiro, experimentamos uma abordagem baseada em Algoritmo Genético (AG) e Otsu para produzir imagens binarizadas. Posteriormente, devido a alguns motivos, incluindo a relevância recente da Segmentação Semântica, seus níveis de abstração e os resultados inviáveis obtidos com AG, estudamos e propusemos uma nova abordagem baseada em \ac{SSN} em duas etapas. Primeiro, usamos uma \ac{SSN} para segmentar as imagens, classificando suas regiões como linhas de plantio ou como solo não plantado. Em seguida, utilizamos a transformada de Radon para reconstruir e melhorar as linhas já segmentadas, tornando-as mais uniformes ou agrupando fragmentos de linhas e plantas soltas. Comparamos nossos resultados com segmentações feitas manualmente por especialistas e os resultados demonstram a eficiência e a viabilidade de nossa abordagem para a tarefa proposta