Detection of leek rust disease under field conditions using hyperspectral proximal sensing and machine learning

Rust disease is an important problem for leek cultivation worldwide. It reduces market value and in extreme cases destroys the entire harvest. Farmers have to resort to periodical full-field fungicide applications to prevent the spread of disease, once every 1 to 5 weeks, depending on the cultivar and weather conditions. This implies an economic cost for the farmer and an environmental cost for society. Hyperspectral sensors have been extensively used to address this issue in research, but their application in the field has been limited to a relatively low number of crops, excluding leek, due to the high investment costs and complex data gathering and analysis associated with these sensors. To fill this gap, a methodology was developed for detecting leek rust disease using hyperspectral proximal sensing data combined with supervised machine learning. First, a hyperspectral library was constructed containing 43,416 spectra with a waveband range of 400-1000 nm, measured under field conditions. Then, an extensive evaluation of 11 common classifiers was performed using the scikit-learn machine learning library in Python, combined with a variety of wavelength selection techniques and preprocessing strategies. The best performing model was a (linear) logistic regression model that was able to correctly classify rust disease with an accuracy of 98.14%, using reflectance values at 556 and 661 nm, combined with the value of the first derivative at 511 nm. This model was used to classify unlabelled hyperspectral images, confirming that the model was able to accurately classify leek rust disease symptoms. It can be concluded that the results in this work are an important step towards the mapping of leek rust disease, and that future research is needed to overcome certain challenges before variable rate fungicide applications can be adopted against leek rust disease

Deep Learning Techniques for In-Crop Weed Identification: A Review

Weeds are a significant threat to the agricultural productivity and the environment. The increasing demand for sustainable agriculture has driven innovations in accurate weed control technologies aimed at reducing the reliance on herbicides. With the great success of deep learning in various vision tasks, many promising image-based weed detection algorithms have been developed. This paper reviews recent developments of deep learning techniques in the field of image-based weed detection. The review begins with an introduction to the fundamentals of deep learning related to weed detection. Next, recent progresses on deep weed detection are reviewed with the discussion of the research materials including public weed datasets. Finally, the challenges of developing practically deployable weed detection methods are summarized, together with the discussions of the opportunities for future research.We hope that this review will provide a timely survey of the field and attract more researchers to address this inter-disciplinary research problem

Weed Recognition in Agriculture: A Mask R-CNN Approach

Recent interdisciplinary collaboration on deep learning has led to a growing interest in its application in the agriculture domain. Weed control and management are some of the crucial tasks in agriculture to maintain high crop productivity. The inception phase of weed control and management is to successfully recognize the weed plants, followed by providing a suitable management plan. Due to the complexities in agriculture images, such as similar colour and texture, we need to incorporate a deep neural network that uses pixel-wise grouping for identifying the plant species. In this thesis, we analysed the performance of one of the most popular deep neural networks aimed to solve the instance segmentation (pixel-wise analysis) problems: Mask R-CNN, for weed plant recognition (detection and classification) using field images and aerial images. We have used Mask R-CNN to recognize the crop plants and weed plants using the Crop/Weed Field Image Dataset (CWFID) for the field image study. However, the CWFID\u27s limitations are that it identifies all weed plants as a single class and all of the crop plants are from a single organic carrot field. We have created a synthetic dataset with 80 weed plant species to tackle this problem and tested it with Mask R-CNN to expand our study. Throughout this thesis, we predominantly focused on detecting one specific invasive weed type called Persicaria Perfoliata or Mile-A-Minute (MAM) for our aerial image study. In general, supervised model outcomes are slow to aerial images, primarily due to large image size and scarcity of well-annotated datasets, making it relatively harder to recognize the species from higher altitudes. We propose a three-level (leaves, trees, forest) hierarchy to recognize the species using Unmanned Aerial Vehicles(UAVs) to address this issue. To create a dataset that resembles weed clusters similar to MAM, we have used a localized style transfer technique to transfer the style from the available MAM images to a portion of the aerial images\u27 content using VGG-19 architecture. We have also generated another dataset at a relatively low altitude and tested it with Mask R-CNN and reached ~92% AP50 using these low-altitude resized images

Detection of Citrus Greening Using Microscopic Imaging

Citrus greening reduces fruit production and quality and will likely result in rapid tree decline and death. Because citrus greening symptoms are usually observed on the leaf surface, detection of citrus greening leaf symptoms can significantly aid in scouting for infected trees and managing the disease, thus reducing its spread and minimizing losses for citrus growers. This article presents the microscopic image analysis using color co-occurrence method to differentiate citrus leaves with eight conditions: greening blotchy mottle, green islands, iron deficiency, manganese deficiency, zinc deficiency, young flush leaves and normal mature leaves. Thirty-nine statistical features were extracted from transformed hue (H), saturation (S), and intensity (I) images using the color co-occurrence method for each leaf sample. The number of extracted texture features was reduced by a stepwise discriminant analysis. A discriminant function based on a measure of the generalized squared distance was used for classification. Three classification models were performed using (1) all leaf conditions, (2) all conditions except young flush leaves and (3) all conditions except young flush leaves and blotchy mottle. The three classification models obtained accuracies of 86.67 %, 95.60 % and 97.33 %, respectively. The overall performance was demonstrated in a confusion matrix. The model HSI_14, which used all conditions except young flush and blotchy mottle, resulted in the best accuracy for positive (96.67 %) and negative (97.5 %) symptoms

Detection of Parthenium Weed (Parthenium hysterophorus L.) and Its Growth Stages Using Artificial Intelligence

Parthenium weed (Parthenium hysterophorus L. (Asteraceae)), native to the Americas, is in the top 100 most invasive plant species in the world. In Australia, it is an annual weed (herb/shrub) of national significance, especially in the state of Queensland where it has infested both agricultural and conservation lands, including riparian corridors. Effective control strategies for this weed (pasture management, biological control, and herbicide usage) require populations to be detected and mapped. However, the mapping is made difficult due to varying nature of the infested landscapes (e.g., uneven terrain). This paper proposes a novel method to detect and map parthenium populations in simulated pastoral environments using Red-Green-Blue (RGB) and/or hyperspectral imagery aided by artificial intelligence. Two datasets were collected in a control environment using a series of parthenium and naturally co-occurring, non-parthenium (monocot) plants. RGB images were processed with a YOLOv4 Convolutional Neural Network (CNN) implementation, achieving an overall accuracy of 95% for detection, and 86% for classification of flowering and non-flowering stages of the weed. An XGBoost classifier was used for the pixel classification of the hyperspectral dataset—achieving a classification accuracy of 99% for each parthenium weed growth stage class; all materials received a discernible colour mask. When parthenium and non-parthenium plants were artificially combined in various permutations, the pixel classification accuracy was 99% for each parthenium and non-parthenium class, again with all materials receiving an accurate and discernible colour mask. Performance metrics indicate that our proposed processing pipeline can be used in the preliminary design of parthenium weed detection strategies, and can be extended for automated processing of collected RGB and hyperspectral airborne unmanned aerial vehicle (UAV) data. The findings also demonstrate the potential for images collected in a controlled, glasshouse environment to be used in the preliminary design of invasive weed detection strategies in the field

Plants Detection, Localization and Discrimination using 3D Machine Vision for Robotic Intra-row Weed Control

Weed management is vitally important in crop production systems. However, conventional herbicide-based weed control can lead to negative environmental impacts. Manual weed control is laborious and impractical for large scale production. Robotic weeding offers a possibility of controlling weeds precisely, particularly for weeds growing close to or within crop rows. The fusion of two-dimensional textural images and three-dimensional spatial images to recognize and localize crop plants at different growth stages were investigated. Images of different crop plants at different growth stages with weeds were acquired. Feature extraction algorithms were developed, and different features were extracted and used to train plant and background classifiers, which also addressed the problems of canopy occlusion and leaf damage. Then, the efficacy and accuracy of the proposed methods in classification were demonstrated by experiments. Currently, the algorithms were only developed and tested for broccoli and lettuce. For broccoli plants, the crop plants detection true positive rate was 93.1%, and the false discover rate was 1.1%, with the average crop-plant-localization error of 15.9 mm. For lettuce plants, the crop plants detection true positive rate was 92.3%, and the false discover rate was 4.0%, with the average crop-plant-localization error of 8.5 mm. The results have shown that 3D imaging based plant recognition algorithms are effective and reliable for crop/weed differentiation

A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities

Deep Learning has been successfully applied to image recognition, speech recognition, and natural language processing in recent years. Therefore, there has been an incentive to apply it in other fields as well. The field of agriculture is one of the most important fields in which the application of deep learning still needs to be explored, as it has a direct impact on human well-being. In particular, there is a need to explore how deep learning models can be used as a tool for optimal planting, land use, yield improvement, production/disease/pest control, and other activities. The vast amount of data received from sensors in smart farms makes it possible to use deep learning as a model for decision-making in this field. In agriculture, no two environments are exactly alike, which makes testing, validating, and successfully implementing such technologies much more complex than in most other industries. This paper reviews some recent scientific developments in the field of deep learning that have been applied to agriculture, and highlights some challenges and potential solutions using deep learning algorithms in agriculture. The results in this paper indicate that by employing new methods from deep learning, higher performance in terms of accuracy and lower inference time can be achieved, and the models can be made useful in real-world applications. Finally, some opportunities for future research in this area are suggested.This work is supported by the R&D Project BioDAgro—Sistema operacional inteligente de informação e suporte á decisão em AgroBiodiversidade, project PD20-00011, promoted by Fundação La Caixa and Fundação para a Ciência e a Tecnologia, taking place at the C-MAST-Centre for Mechanical and Aerospace Sciences and Technology, Department of Electromechanical Engineering of the University of Beira Interior, Covilhã, Portugal.info:eu-repo/semantics/publishedVersio

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