Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses

Image analysis for ornamental crops is discussed with examples from the bedding plant industry. Feed-forward artificial neural networks are used to segment top and side view images of three contrasting species of bedding plants. The segmented images provide objective measurements of leaf and flower cover, colour, uniformity and leaf canopy height. On each imaging occasion, each pack was scored for quality by an assessor panel and it is shown that image analysis can explain 88.5%, 81.7% and 70.4% of the panel quality scores for the three species, respectively. Stereoscopy for crop height and uniformity is outlined briefly. The methods discussed here could be used for crop grading at marketing or for monitoring and assessment of growing crops within a glasshouse during all stages of production

Designing an FPGA Synthesizable Computer Vision Algorithm to Detect the Greening of Potatoes

Potato quality control has improved in the last years thanks to automation techniques like machine vision, mainly making the classification task between different quality degrees faster, safer and less subjective. In our study we are going to design a computer vision algorithm for grading of potatoes according to the greening of the surface color of potato. The ratio of green pixels to the total number of pixels of the potato surface is found. The higher the ratio the worse is the potato. First the image is converted into serial data and then processing is done in RGB colour space. Green part of the potato is also shown by de-serializing the output. The same algorithm is then synthesized on FPGA and the result shows thousand times speed improvement in case of hardware synthesis.Comment: 5 pages, 8 figures, 2 tables, "Published with International Journal of Engineering Trends and Technology (IJETT)" ISSN:2231-5381. http://www.ijettjournal.org. published by seventh sense research grou

An In-field Automatic Wheat Disease Diagnosis System

Crop diseases are responsible for the major production reduction and economic losses in agricultural industry world- wide. Monitoring for health status of crops is critical to control the spread of diseases and implement effective management. This paper presents an in-field automatic wheat disease diagnosis system based on a weakly super- vised deep learning framework, i.e. deep multiple instance learning, which achieves an integration of identification for wheat diseases and localization for disease areas with only image-level annotation for training images in wild conditions. Furthermore, a new in-field image dataset for wheat disease, Wheat Disease Database 2017 (WDD2017), is collected to verify the effectiveness of our system. Under two different architectures, i.e. VGG-FCN-VD16 and VGG-FCN-S, our system achieves the mean recognition accuracies of 97.95% and 95.12% respectively over 5-fold cross-validation on WDD2017, exceeding the results of 93.27% and 73.00% by two conventional CNN frameworks, i.e. VGG-CNN-VD16 and VGG-CNN-S. Experimental results demonstrate that the proposed system outperforms conventional CNN architectures on recognition accuracy under the same amount of parameters, meanwhile main- taining accurate localization for corresponding disease areas. Moreover, the proposed system has been packed into a real-time mobile app to provide support for agricultural disease diagnosis.Comment: 15 page

Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data

Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate the disease modifying drug development and ultimately help to prevent millions of total joint replacement surgeries performed annually. Here, we present a multi-modal machine learning-based OA progression prediction model that utilizes raw radiographic data, clinical examination results and previous medical history of the patient. We validated this approach on an independent test set of 3,918 knee images from 2,129 subjects. Our method yielded area under the ROC curve (AUC) of 0.79 (0.78-0.81) and Average Precision (AP) of 0.68 (0.66-0.70). In contrast, a reference approach, based on logistic regression, yielded AUC of 0.75 (0.74-0.77) and AP of 0.62 (0.60-0.64). The proposed method could significantly improve the subject selection process for OA drug-development trials and help the development of personalized therapeutic plans

Image Analysis and Machine Learning in Agricultural Research

Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed. Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could help with agricultural data collection. In the first chapter, information related to different types of imaging (e.g., RGB, multi/hyperspectral, and thermal imaging) was explored in detail for its advantages in different agriculture applications. The process of image analysis demonstrated how target features were extracted for analysis including shape, edge, texture, and color. After acquiring features information, machine learning can be used to automatically detect or predict features of interest such as disease severity. In the second chapter, case studies of different agricultural applications were demonstrated including: 1) leaf damage symptoms, 2) stress evaluation, 3) plant growth evaluation, 4) stand/insect counting, and 5) evaluation for produce quality. Case studies showed that the use of image analysis is often more advantageous than visual rating. Advantages of image analysis include increased objectivity, speed, and more reproducibly reliable results. In the third chapter, machine learning was explored using romaine lettuce images from RD4AG to automatically grade for bolting and compactness (two of the important parameters for lettuce quality). Although the accuracy is at 68.4 and 66.6% respectively, a much larger data base and many improvements are needed to increase the model accuracy and reliability. With the advancement in cameras, computers with high computing power, and the development of different algorithms, image analysis and machine learning have the potential to replace part of the labor and improve the current data collection procedure in agricultural research. Advisor: Gary L. Hei

Cocoa Care - An Android Application for Cocoa Disease Identification

India is an agricultural country. The correct and timely identification of diseases in crops is very much essential in agriculture. To obtain more valuable products, a product quality control is basically mandatory. Cocoa is an economically important crop that nowadays enlarges its production in southern India. To assist the farmers growing cocoa, we developed an android application Cocoa-Care. This application automatically identifies the diseases of cocoa crops, thereby helps the farmers who have little or no information about the disease. This application is developed by applying digital image processing techniques on the diseased cocoa images. Our approach replaces the manual disease inspection by the android application that identifies the cocoa disease from the captured image and suggests the possible remedies for the farmer. We used moment based texture features for the image representation and description. The matching is performed by nearest neighbor classifier. The results obtained are promising and this application can be used in the real time

Wheat stripe rust grading by deep learning with attention mechanism and images from mobile devices

Wheat stripe rust is one of the main wheat diseases worldwide, which has significantly adverse effects on wheat yield and quality, posing serious threats on food security. Disease severity grading plays a paramount role in stripe rust disease management including breeding disease-resistant wheat varieties. Manual inspection is time-consuming, labor-intensive and prone to human errors, therefore, there is a clearly urgent need to develop more effective and efficient disease grading strategy by using automated approaches. However, the differences between wheat leaves of different levels of stripe rust infection are usually tiny and subtle, and, as a result, ordinary deep learning networks fail to achieve satisfying performance. By formulating this challenge as a fine-grained image classification problem, this study proposes a novel deep learning network C-DenseNet which embeds Convolutional Block Attention Module (CBAM) in the densely connected convolutional network (DenseNet). The performance of C-DenseNet and its variants is demonstrated via a newly collected wheat stripe rust grading dataset (WSRgrading dataset) at Northwest A&F University, Shaanxi Province, China, which contains a total of 5,242 wheat leaf images with 6 levels of stripe rust infection. The dataset was collected by using various mobile devices in the natural field condition. Comparative experiments show that C-DenseNet with a test accuracy of 97.99% outperforms the classical DenseNet (92.53%) and ResNet (73.43%). GradCAM++ network visualization also shows that C-DenseNet is able to pay more attention to the key areas in making the decision. It is concluded that C-DenseNet with an attention mechanism is suitable for wheat stripe rust disease grading in field conditions

Dynamics of agrarian landscapes in Western Thailand : Agro-ecological zonation and agricultural transformations in Kanjanaburi Province: hypotheses for improving farming systems sustainability

Ce document traite de la zonation agroécologique à petite échelle, comme outil essentiel dans la recherche orientée sur les systèmes agraires en vue du développement. Ces systèmes sont définis comme modes d'exploitation adaptés à l'environnement (naturel et humain) y compris échanges de produit et patrimoine culturel; l'étude comprend systèmes de production et de culture, et types d'utilisation des sols. Les diverses relations entre éléments sont analysées dans l'espace et le temps de façon à dégager la dynamique des transformations. Le projet a fait intervenir des équipes pluridisciplinaires comprenant agronomes et spécialistes des ressources naturelles en sociologie et télédétection; le tout aux niveaux de la parcelle et de l'exploitation agricole. Le texte, qui comporte un glossaire technique précis, est illustré de six clichés en couleurs (cultures de maïs, cotonnier, manioc, manguiers) et d'une image digitale en couleurs d'une partie de l'ouest de la Thaïlande vue du satellite Landsat-T

Detection of Bacterial Blight on Pomegranate Leaf

In india, agricultural field plays vital role in the development of India. Smart farming is about empowering today’s farmers with the decision tools and automation technologies that seamlessly integrate products, knowledge and services for better productivity, quality and profit. In this paper, a solution for the detection of pomegranate leaf disease and also the solution for that disease after detection are proposed. The proposed system mainly consist image preprocessing, feature extraction, clustering and classification. The first steps consists image preprocessing in which images are resized. In second step, feature extraction is carried out. Color, morphology and color coherence vector features are used for the purpose of feature extraction . K-means clustering technique is used for partitioning training dataset into desired number of clusters according the features that has been extracted from the fruit images. Then the next step includes training and classification. Support Vector Machine approach is used for classification. DOI: 10.17762/ijritcc2321-8169.15064

A Brief Review on Plant Leaf Disease Detection Using Auto Adaptive Approach

This proposal is regarding automatic detection of diseases and pathological part present within the leaf pictures of plants and even within the agriculture Crop production it is through with advancement of technology that helps in farming to extend the production. Primarily there is downside of detection accuracy and in neural network approach support vector machine (SVM) is exist already. During this analysis proposal, a completely unique approach can design to extend accuracy victimization KNN. During this analysis work, we are going to work upon the advancement of the plant diseases prediction techniques and going to propose a completely unique approach for the detection rule