Real-time image processing for crop/weed discrimination in maize fields

This paper presents a computer vision system that successfully discriminates between weed patches and crop rows under uncontrolled lighting in real-time. The system consists of two independent subsystems, a fast image processing delivering results in real-time (Fast Image Processing, FIP), and a slower and more accurate processing (Robust Crop Row Detection, RCRD) that is used to correct the first subsystem's mistakes. This combination produces a system that achieves very good results under a wide variety of conditions. Tested on several maize videos taken of different fields and during different years, the system successfully detects an average of 95% of weeds and 80% of crops under different illumination, soil humidity and weed/crop growth conditions. Moreover, the system has been shown to produce acceptable results even under very difficult conditions, such as in the presence of dramatic sowing errors or abrupt camera movements. The computer vision system has been developed for integration into a treatment system because the ideal setup for any weed sprayer system would include a tool that could provide information on the weeds and crops present at each point in real-time, while the tractor mounting the spraying bar is movin

Técnicas de visión por computador para la detección del verdor y la detección de obstáculos en campos de maíz

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia Artificial, leída el 22/06/2017There is an increasing demand in the use of Computer Vision techniques in Precision Agriculture (PA) based on images captured with cameras on-board autonomous vehicles. Two techniques have been developed in this research. The rst for greenness identi cation and the second for obstacle detection in maize elds, including people and animals, for tractors in the RHEA (robot eets for highly e ective and forestry management) project, equipped with monocular cameras on-board the tractors. For vegetation identi cation in agricultural images the combination of colour vegetation indices (CVIs) with thresholding techniques is the usual strategy where the remaining elements on the image are also extracted. The main goal of this research line is the development of an alternative strategy for vegetation detection. To achieve our goal, we propose a methodology based on two well-known techniques in computer vision: Bag of Words representation (BoW) and Support Vector Machines (SVM). Then, each image is partitioned into several Regions Of Interest (ROIs). Afterwards, a feature descriptor is obtained for each ROI, then the descriptor is evaluated with a classi er model (previously trained to discriminate between vegetation and background) to determine whether or not the ROI is vegetation...Cada vez existe mayor demanda en el uso de t ecnicas de Visi on por Computador en Agricultura de Precisi on mediante el procesamiento de im agenes captadas por c amaras instaladas en veh culos aut onomos. En este trabajo de investigaci on se han desarrollado dos tipos de t ecnicas. Una para la identi caci on de plantas verdes y otra para la detecci on de obst aculos en campos de ma z, incluyendo personas y animales, para tractores del proyecto RHEA. El objetivo nal de los veh culos aut onomos fue la identi caci on y eliminaci on de malas hierbas en los campos de ma z. En im agenes agr colas la vegetaci on se detecta generalmente mediante ndices de vegetaci on y m etodos de umbralizaci on. Los ndices se calculan a partir de las propiedades espectrales en las im agenes de color. En esta tesis se propone un nuevo m etodo con tal n, lo que constituye un objetivo primordial de la investigaci on. La propuesta se basa en una estrategia conocida como \bolsa de palabras" conjuntamente con un modelo se aprendizaje supervisado. Ambas t ecnicas son ampliamente utilizadas en reconocimiento y clasi caci on de im agenes. La imagen se divide inicialmente en regiones homog eneas o de inter es (RIs). Dada una colecci on de RIs, obtenida de un conjunto de im agenes agr colas, se calculan sus caracter sticas locales que se agrupan por su similitud. Cada grupo representa una \palabra visual", y el conjunto de palabras visuales encontradas forman un \diccionario visual". Cada RI se representa por un conjunto de palabras visuales las cuales se cuanti can de acuerdo a su ocurrencia dentro de la regi on obteniendo as un vector-c odigo o \codebook", que es descriptor de la RI. Finalmente, se usan las M aquinas de Vectores Soporte para evaluar los vectores-c odigo y as , discriminar entre RIs que son vegetaci on del resto...Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Local Binary Pattern based algorithms for the discrimination and detection of crops and weeds with similar morphologies

In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact on the human health, economy and environment. Before weeds are resistant to herbicides and respond better to weed control strategies, it is necessary to control them in the fallow, pre-sowing, early post-emergent and in pasture phases. Moreover, the development of herbicide resistance in weeds is the driving force for inventing precision and automation weed treatments. Various weed detection techniques have been developed to identify weed species in crop fields, aimed at improving the crop quality, reducing herbicide and water usage and minimising environmental impacts. In this thesis, Local Binary Pattern (LBP)-based algorithms are developed and tested experimentally, which are based on extracting dominant plant features from camera images to precisely detecting weeds from crops in real time. Based on the efficient computation and robustness of the first LBP method, an improved LBP-based method is developed based on using three different LBP operators for plant feature extraction in conjunction with a Support Vector Machine (SVM) method for multiclass plant classification. A 24,000-image dataset, collected using a testing facility under simulated field conditions (Testbed system), is used for algorithm training, validation and testing. The dataset, which is published online under the name “bccr-segset”, consists of four subclasses: background, Canola (Brassica napus), Corn (Zea mays), and Wild radish (Raphanus raphanistrum). In addition, the dataset comprises plant images collected at four crop growth stages, for each subclass. The computer-controlled Testbed is designed to rapidly label plant images and generate the “bccr-segset” dataset. Experimental results show that the classification accuracy of the improved LBP-based algorithm is 91.85%, for the four classes. Due to the similarity of the morphologies of the canola (crop) and wild radish (weed) leaves, the conventional LBP-based method has limited ability to discriminate broadleaf crops from weeds. To overcome this limitation and complex field conditions (illumination variation, poses, viewpoints, and occlusions), a novel LBP-based method (denoted k-FLBPCM) is developed to enhance the classification accuracy of crops and weeds with similar morphologies. Our contributions include (i) the use of opening and closing morphological operators in pre-processing of plant images, (ii) the development of the k-FLBPCM method by combining two methods, namely, the filtered local binary pattern (LBP) method and the contour-based masking method with a coefficient k, and (iii) the optimal use of SVM with the radial basis function (RBF) kernel to precisely identify broadleaf plants based on their distinctive features. The high performance of this k-FLBPCM method is demonstrated by experimentally attaining up to 98.63% classification accuracy at four different growth stages for all classes of the “bccr-segset” dataset. To evaluate performance of the k-FLBPCM algorithm in real-time, a comparison analysis between our novel method (k-FLBPCM) and deep convolutional neural networks (DCNNs) is conducted on morphologically similar crops and weeds. Various DCNN models, namely VGG-16, VGG-19, ResNet50 and InceptionV3, are optimised, by fine-tuning their hyper-parameters, and tested. Based on the experimental results on the “bccr-segset” dataset collected from the laboratory and the “fieldtrip_can_weeds” dataset collected from the field under practical environments, the classification accuracies of the DCNN models and the k-FLBPCM method are almost similar. Another experiment is conducted by training the algorithms with plant images obtained at mature stages and testing them at early stages. In this case, the new k-FLBPCM method outperformed the state-of-the-art CNN models in identifying small leaf shapes of canola-radish (crop-weed) at early growth stages, with an order of magnitude lower error rates in comparison with DCNN models. Furthermore, the execution time of the k-FLBPCM method during the training and test phases was faster than the DCNN counterparts, with an identification time difference of approximately 0.224ms per image for the laboratory dataset and 0.346ms per image for the field dataset. These results demonstrate the ability of the k-FLBPCM method to rapidly detect weeds from crops of similar appearance in real time with less data, and generalize to different size plants better than the CNN-based methods

Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields

Producción CientíficaSite-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively

Improving field management by machine vision - a review

Growing population of people around the world and thus increasing demand to food products as well as high tendency for declining the cost of operations and environmental preserving cares intensify inclination toward the application of variable rate systems for agricultural treatments, in which machine vision as a powerful appliance has been paid vast attention by agricultural researchers and farmers as this technology consumers. Various applications have introduced for machine vision in different fields of agricultural and food industry till now that confirms the high potential of this approach for inspection of different parameters affecting productivity. Computer vision has been utilized for quantification of factors affecting crop growth in field; such as, weed, irrigation, soil quality, plant nutrients and fertilizers in several cases. This paper presents some of these successful applications in addition to representing an introduction to machine vision

Semantic Segmentation based deep learning approaches for weed detection

Global increase in herbicide use to control weeds has led to issues such as evolution of herbicide-resistant weeds, off-target herbicide movement, etc. Precision agriculture advocates Site Specific Weed Management (SSWM) application to achieve precise and right amount of herbicide spray and reduce off-target herbicide movement. Recent advancements in Deep Learning (DL) have opened possibilities for adaptive and accurate weed recognitions for field based SSWM applications with traditional and emerging spraying equipment; however, challenges exist in identifying the DL model structure and train the model appropriately for accurate and rapid model applications over varying crop/weed growth stages and environment. In our study, an encoder-decoder based DL architecture was proposed that performs pixel-wise Semantic Segmentation (SS) classifications of crop, soil, and weed patches in the fields. The objective of this study was to develop a robust weed detection algorithm using DL techniques that can accurately and reliably locate weed infestations in low altitude Unmanned Aerial Vehicle (UAV) imagery with acceptable application speed. Two different encoder-decoder based SS models of LinkNet and UNet were developed using transfer learning techniques. We performed various measures such as backpropagation optimization and refining of the dataset used for training to address the class-imbalance problem which is a common issue in developing weed detection models. It was found that LinkNet model with ResNet18 as the encoder section and use of ‘Focal loss’ loss function was able to achieve the highest mean and class-wise Intersection over Union scores for different class categories while performing predictions on unseen dataset. The developed state-of-art model did not require a large amount of data during training and the techniques used to develop the model in our study provides a propitious opportunity that performs better than the existing SS based weed detections models. The proposed model integrates a futuristic approach to develop a model that could be used for weed detection on aerial imagery from UAV and perform real-time SSWM applications Advisor: Yeyin Sh

Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods

Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the combination of Local Binary Pattern (LBP) operators, for the extraction of crop leaf textural features and Support vector machine (SVM) method, for multiclass plant classification. This paper presents the first investigation of the accuracy of the combined LBP algorithms, trained using a large dataset of canola, radish and barley leaf images captured by a testing facility under simulated field conditions. The dataset has four subclasses, background, canola, corn, and radish, with 24,000 images used for training and 6000 images, for validation. The dataset is referred herein as “bccr-segset” and published online. In each subclass, plant images are collected at four crop growth stages. Experimentally, the algorithm demonstrates plant classification accuracy as high as 91.85%, for the four classes. © 2018 China Agricultural Universit