Performance analysis of support vector machine for early identification of citrus diseases

Early citrus disease detection is necessary for optimum citrus productivity. But detecting a citrus disease at an early stage requires expert views or laboratory tests. But getting an expert view of all time is impossible for rural farmers. The present study aimed to create a low-cost, intelligent, affordable citrus disease classification system. This study offered a Support Vector Machine (SVM) based smart classification method for categorizing various citrus diseases. Citrus photos were subjected to a variety of image processing techniques to categorize the diseases using SVM and the kernel. Prior to classification, the images were segmented and the hue channel threshold value was used to differentiate the diseased area from the remaining portion of the image. The segmented image’s color and grey domains were used to extract 13 different texture and color features. This study outlined three different SVM kernel types- Linear, Gaussian, and Polynomial, and evaluated their accuracy and confusion matrix performances. The Radial Based Function with a polynomial kernel derived from the SVM outperformed the SVM's linear and Gaussian kernel

Image multi-level-thresholding with Mayfly optimization

Image thresholding is a well approved pre-processing methodology and enhancing the image information based on a chosen threshold is always preferred. This research implements the mayfly optimization algorithm (MOA) based image multi-level-thresholding on a class of benchmark images of dimension 512x512x1. The MOA is a novel methodology with the algorithm phases, such as; i) Initialization, ii) Exploration with male-mayfly (MM), iii) Exploration with female-mayfly (FM), iv) Offspring generation and, v) Termination. This algorithm implements a strict two-step search procedure, in which every Mayfly is forced to attain the global best solution. The proposed research considers the threshold value from 2 to 5 and the superiority of the result is confirmed by computing the essential Image quality measures (IQM). The performance of MOA is also compared and validated against the other procedures, such as particle-swarm-optimization (PSO), bacterial foraging optimization(BFO), firefly-algorithm(FA), bat algorithm (BA), cuckoo search(CS) and moth-flame optimization (MFO) and the attained p-value of Wilcoxon rank test confirmed the superiority of the MOA compared with other algorithms considered in this wor

Resistance inducers and organic fertilizer in Citrus sinensis [L.] Osbeck infected with Candidatus Liberibacter asiaticus bacteria

Objective: evaluate the effect of three resistance inducers and an organic fertilizer on the titles of Candidatus Liberibacter asiaticus in Citrus sinensis (L.) Obseck cv. Valencia. Diseño/metodología: The treatments consisted of Vacciplant Max and UPL-08, Fosetil aluminio. Likewise, BIO-FOM was applied on the periphery of the tree, presenting moisture for the absorption of nutrients. The variables evaluated were fruit weight, equatorial diameter, skin thickness, °BRIX, severity and chlorophyll in each of the five treatments, which consisted of 20 repetitions. Results:  the fruits of trees treated with Vacciplant Max had lower shell thickness and a higher °BRIX content. In addition, the highest chlorophyll index was achieved with BIO-FOM fertilizer. However, none of the evaluated treatments significantly decreased the severity percentage. Findings/conclusions: The best treatment against Huanglongbing was fosetyl aluminio by conferring greater fruit weight and diameter.Objective: To evaluate the effect of three resistance inducers and an organic fertilizer on the titles of CandidatusLiberibacter asiaticus in Citrus sinensis (L.) Obseck cv. Valencia.Design/methodology/approach: The treatments consisted of Vacciplant Max and UPL-08, Fosetil aluminum. Likewise, BIO-FOM was applied on the periphery of the trees, with moisture for nutrients absorption. The evaluated variables were fruit weight, equatorial diameter, skin thickness, °BRIX, severity and chlorophyll, in each of the five treatments, which consisted of 20 repetitions.Results: The fruits of the trees treated with Vacciplant Max had lower skin thickness and a higher °BRIX. Also, the highest chlorophyll index was recorded with BIO-FOM fertilizer. However, none of the evaluated treatments significantly decreased the fruit harshness.Findings/conclusions: The best treatment against Huanglongbing was fosetyl aluminum which conferred greater fruit weight and diamete

Texture profile of filmogenic solutions with potential application for seed biodegradable coatings / Perfil de textura de soluções filmogénicas com potencial aplicação de revestimentos biodegradáveis de sementes

Seed coating has been used in order to minimize environmental and human health damage generated by the use of conventional treatments. Therefore, the aim of this work was to evaluate the influence of the contents of cassava starch (0.5 and 5% (m m-1)), glycerol (5 and 50% (m m-1)) in relation to the starch mass, and pH (5.5 and 6.5) on the texture parameters (hardness, adhesiveness, fracture, cohesiveness, elasticity and gumminess) of filmogenic solutions with the potential to prepare seed coatings. The adhesiveness, cohesiveness, elasticity and gumminess, were influenced by the variables in the studied range. In order to evaluate the coatings produced on seed germination, future work is necessary

A Framework for Crop Disease Detection Using Feature Fusion Method

Crop disease detection methods vary from traditional machine learning, which uses Hand-Crafted Features (HCF) to the current deep learning techniques that utilize deep features. In this study, a hybrid framework is designed for crop disease detection using feature fusion. Convolutional Neural Network (CNN) is used for high level features that are fused with HCF. Cepstral coefficients of RGB images are presented as one of the features along with the other popular HCF. The proposed hybrid model is tested on the whole leaf images and also on the image patches which have individual lesions. The experimental results give an enhanced performance with a classification accuracy of 99.93% for the whole leaf images and 99.74% for the images with individual lesions. The proposed model also shows a significant improvement in comparison to the state-of-art techniques. The improved results show the prominence of feature fusion and establish cepstral coefficients as a pertinent feature for crop disease detection

Un enfoque para la detección de enfermedades de las plantas utilizando técnicas de aprendizaje profundo

Agriculture is the backbone of Indian economy. Conventional farming systems are no longer being followed by our generation, due to lack of knowledge and expertise. Advancement of technologies pave a path that make a transition from traditional farming methods to smart agriculture by automating the processes involved. Challenges faced by today’s agriculture are depletion of soil nutrients and diseases caused by pests which lead to low productivity, irrigation problems, soil erosion, shortage of storage facilities, availability of quality seeds, lack of transportation, poor marketing etc. Among all these challenges in agriculture, prediction of diseases remains a major issue to be addressed. Identifying diseases based on visual inspection is the traditional way of farming which needs knowledge and experience to handle. Automating the process of detecting and identifying through visual inspection (cognitive) is the motivation behind this work. This is made possible with the availability of images of the plant or parts of plants, since most diseases are reflected on the leaves. A deep learning network architecture named Plant Disease Detection Network PDDNet-cv and a transfer learning approach of identifying diseases in plants were proposed. Our proposed system is compared with VGG19, ResNet50, InceptionResNetV2, the state-of-the-art methods reported in [9, 13, 5] and the results show that our method is significantly performing better than the existing systems. Our proposed PDDNet-cv has achieved average classification accuracy of 99.09% in detecting different classes of diseases. The proposed not so deep architecture is performing well compared to other deep learning architectures in terms of performance and computational time.La agricultura es la columna vertebral de la economía india. Los sistemas agrícolas convencionales ya no están siendo seguidos por nuestra generación, debido a la falta de conocimiento y experiencia. El avance de las tecnologías allana un camino que hace una transición de los métodos agrícolas tradicionales a la agricultura inteligente mediante la automatización de los procesos involucrados. Los desafíos que enfrenta la agricultura actual son el agotamiento de los nutrientes del suelo, las enfermedades causadas por plagas que conducen a una baja productividad, los problemas de riego, la erosión del suelo, la escasez de instalaciones de almacenamiento, la disponibilidad de semillas de calidad, la falta de transporte, la mala comercialización, etc. Entre todos estos desafíos en la agricultura, la predicción de enfermedades sigue siendo un tema importante que debe abordarse. La identificación de enfermedades basadas en la inspección visual es la forma tradicional de cultivo que necesita el conocimiento y la experiencia para manejarlas que obtiene un buen rendimiento. Automatizar el proceso de detección e identificación a través de la inspección visual (cognitiva) es la motivación detrás de este trabajo. Esto es posible gracias a la disponibilidad de imágenes de la planta o partes de plantas, ya que la mayoría de las enfermedades se reflejan en las hojas. Se propuso una arquitectura de red de aprendizaje profundo llamada Red de Detección de Enfermedades de las plantas por sus siglas en inglés (Plant Disease Detection Network PDDNet-cv) y un enfoque de aprendizaje por transferencia para identificar enfermedades en las plantas. Nuestro sistema propuesto se compara con VGG19, ResNet50, InceptionResNetV2, los métodos de vanguardia reportados en [9, 13, 5] y los resultados muestran que nuestro método está funcionando significativamente mejor que los sistemas existentes. Nuestra propuesta PDDNet-cv ha logrado una precisión de clasificación promedio del 99,09% en la detección de diferentes clases de enfermedades. La arquitectura no tan profunda propuesta, está funcionando bien en comparación con otras arquitecturas de aprendizaje profundo en términos de rendimiento y tiempo computacional

Comparative Analysis of Fruit Disease Identification Methods: A Comprehensive Study

The need for accurate and efficient technologies for recognising and controlling fruit diseases has increased due to the rising global demand for high-quality agricultural products. This study focuses on the advantages, disadvantages, and potential practical applications of a range of methods for identifying fecundities. Thanks to developments like improved imaging, machine learning, and data analysis tools, old methods of disease diagnosis have altered as technology has developed. The study compares older methods like visual observation, manual symptom correlation, spectroscopy, and chemical procedures with more contemporary methods like computer vision, autonomous learning algorithms, and sensor-based technologies. Precision, efficiency, cost, scalability, and ease of use are used to describe each method's effectiveness. The article reviews the research examples and practical applications of fruit endocrine disease detection in different cultivars and areas to provide a thorough comparison. This comparison focuses on the variations in disease prevalence and the ways that alternative treatments can be customised to certain situations.It is for this reason that this study offers useful information on how the methods for detecting fruit rot have evolved through time. It emphasises the significance of utilising technological advances to enhance the accuracy, effectiveness, and long-term sustainability of the management of agricultural diseases. Based on the unique requirements of their various agricultural systems, this analysis can assist researchers, practitioners, and policymakers in selecting the most effective methods for identifying fruit diseases

Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. As a result, designing an automated detection system for citrus plant diseases is important. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, leading us to apply them to the challenge of recognizing citrus fruit and leaf diseases. In this paper, an integrated approach is used to suggest a convolutional neural networks (CNNs) model. The proposed CNN model is intended to differentiate healthy fruits and leaves from fruits/leaves with common citrus diseases such as Black spot, canker, scab, greening, and Melanose. The proposed CNN model extracts complementary discriminative features by integrating multiple layers. The CNN model was checked against many state-of-the-art deep learning models on the Citrus and PlantVillage datasets. The experimental results indicate that the CNN Model outperforms the competitors on a number of measurement metrics. The CNN Model has a test accuracy of 94.55 percent, making it a valuable decision support tool for farmers looking to classify citrus fruit/leaf diseases

A Review on Advances in Automated Plant Disease Detection

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images

Identificación de síntomas de Huanglongbing en hojas de cítricos mediante técnicas de deep learning

Los sistemas de visión artificial permiten automatizar tareas que requieren de personal entrenado para la identificación de características relevantes de determinados objetos. En este trabajo se describe el desarrollo de una aplicación móvil que utiliza técnicas de deep learning para identificar síntomas de Huanglongbing y carencias nutricionales en hojas de árboles cítricos. Se evaluaron los modelos de aprendizaje por trasferencia MobileNet e Inception utilizando Tensorflow y Python. Se generó una aplicación móvil para Android que logró clasificar correctamente el 89 % de las imágenes de hojas de un conjunto de evaluación utilizando el modelo MobileNet. La aplicación generada permitirá mejorar la identificación de síntomas en hojas de árboles cítricos durante los monitoreos realizados en plantaciones citrícolas.Sociedad Argentina de Informática e Investigación Operativ