Resnet-Based Approach For Detection And Classification Of Plant Leaf Diseases

Plant diseases may cause large yield losses, endangering both the stability of the economy and the supply of food. Convolutional Neural Networks (CNNs), in particular, are deep neural networks that have shown remarkable effectiveness in completing image categorization tasks, often outperforming human ability. It has numerous applications in voice processing, picture and video processing, and natural language processing (NLP). It has also grown into a centre for research on plant protection in agriculture, including the assessment of pest ranges and the diagnosis of plant diseases. In two plant phenotyping tasks, the function of a CNN (Convolutional Neural Networks) structure based on Residual Networks (ResNet) is investigated in this study. The majority of current studies on Species Recognition (SR) and plant infection detection have used balanced datasets for accuracy and experimentation as the evaluation criteria. This study, however, made use of an unbalanced dataset with an uneven number of pictures, organised the data into several test cases and classes, conducted data augmentation to improve accuracy, and—most importantly—used multiclass classifier assessment settings that were helpful for an asymmetric class distribution. Furthermore with all these frequent issues, the paper addresses selecting the size of the data collection, classifier depth, necessary training time, and assessing the efficacy of the classifier when using various test scenarios. The Species Recognising (SR) and Identifying of Health and Infection Leaves (IHIL) tasks in this study have shown substantial improvement in performance for the ResNet 20 (V2) architecture, with Precision of 91.84% & 84.00%, Recall of 91.67% and 83.14%, and F1 scores of 91.49% & 83.19%, respectively. &nbsp

CONSTRUCTION OF PRACTICAL SYSTEM FOR AUTOMATIC PLANT DIAGNOSIS

An automatic plant diagnosis system that is available anywhere and anytime is required. Although systems are built based on machine learning methods, including deep learning methods, have been proposed so far, there are very few systems that are available on farms practically. In this study, we aim to create a highly accurate and robust plant disease diagnosis system which is available from a web site on actual farms and various places. First, we achieved the accuracy of 96.4% for the 13 class classification task of cucumber leaf: 71,615 images infected by 12 major diseases and 17,484 healthy images. In addition, we built a practical Web system using the created classifier as a part of our project of the Ministry of Agriculture, Forestry and Fisheries. Users can access the Web system we have built through the native mobile application created by NTT DATA Corporation. We will open the application to the public in recent years. This automatic diagnosis system for plant diseases has already been released only to the people involved in the project and has been verified by the system in order. However, the accuracy is 56.1% for unknown images which was taken in different fields and different shooting dates with the training dataset. This result did not satisfy our expectations. In this paper, This problem is described in detail. As future works, we will deal with the problem in the versatility of our classifier

A PCA-SMO Based Hybrid Classification Model for Predictions in Precision Agriculture

The human population is growing at an extremely rapid rate, the demand of food supplies for the survival and sustainability of life is a gleaming challenge. Each living being in the planet gets bestowed with the healthy food to remain active and healthy. Agriculture is a domain which is extremely important as it provides the fundamental resources for survival in terms of supplying food and thus the economy of the entire world is highly dependent on agricultural production. The agricultural production is often affected by various environmental and geographical factors which are difficult to avoid being part of nature. Thus, it requires proactive mitigation plans to reduce any detrimental effect caused by the imbalance of these factors. Precision agriculture is an approach that incorporates information technology in agriculture management, the needs of crops and farming fields are fulfilled to optimized crop health and resultant crop production. The proposed study involves an ambient intelligence-based implementation using machine learning to classify diseases in tomato plants based on the images of its leaf dataset. To analytically evaluate the performance of the framework, a publicly available plant-village dataset is used which is transformed to appropriate form using one-hot encoding technique to meet the needs of the machine learning algorithm. The transformed data is dimensionally reduced by Principal Component Analysis (PCA) technique and further the optimal parameters are selected using Spider Monkey Optimization (SMO) approach. The most relevant features as selected using the Hybrid PCA-SMO technique fed into a Deep Neural Networks (DNN) model to classify the tomato diseases. The optimal performance of the DNN model after implementing dimensionality reduction by Hybrid PCA-SMO technique reached at 99% accuracy was achieved in training and 94% accuracy was achieved after testing the model for 20 epochs. The proposed model is evaluated based on accuracy and loss rate metrics; it justifies the superiority of the approach

Deep learning for image-based cassava disease detection

Open Access Journal; Published online: 27 Oct 2017Cassava is the third largest source of carbohydrates for human food in the world but is vulnerable to virus diseases, which threaten to destabilize food security in sub-Saharan Africa. Novel methods of cassava disease detection are needed to support improved control which will prevent this crisis. Image recognition offers both a cost effective and scalable technology for disease detection. New deep learning models offer an avenue for this technology to be easily deployed on mobile devices. Using a dataset of cassava disease images taken in the field in Tanzania, we applied transfer learning to train a deep convolutional neural network to identify three diseases and two types of pest damage (or lack thereof). The best trained model accuracies were 98% for brown leaf spot (BLS), 96% for red mite damage (RMD), 95% for green mite damage (GMD), 98% for cassava brown streak disease (CBSD), and 96% for cassava mosaic disease (CMD). The best model achieved an overall accuracy of 93% for data not used in the training process. Our results show that the transfer learning approach for image recognition of field images offers a fast, affordable, and easily deployable strategy for digital plant disease detection

Aplicación móvil para la identificación de variedades de Manihot esculenta Crantz cultivadas en Misiones mediante técnicas de deep learning

El cultivo de Manihot esculenta Crantz, conocida popularmente en la provincia de Misiones como mandioca, es muy importante ya que el aporte de hidratos de carbono de sus raíces complementa la canasta familiar, asimismo el requerimiento por parte de la industria de la raíz de la mandioca se incrementa día a día gracias al uso de la fécula de mandioca en la elaboración de productos aptos para celíacos. En Misiones se cultivan distintas variedades de mandioca, cada una de estas posee cualidades que la hacen aptas para distintos fines. A sim-ple vista las plantas de mandioca son similares para todas las variedades, pero los expertos del Instituto Nacional de Tecnología Agropecuaria de Misiones se en-cuentran trabajando en la generación de un registro único de variedades que sirva como guía para los productores. En este trabajo se plantea el uso de técnicas de deep learning para identificar mediante una imagen de la hoja o del tallo de una planta de mandioca la variedad correspondiente. Para esto se desarrolló una apli-cación móvil utilizando el modelo de aprendizaje por trasferencia MobileNet con Tensorflow, mediante la misma se logró clasificar de manera correcta el 92 % de las imágenes de tallos y el 81 % de imágenes de hojas de mandioca.Sociedad Argentina de Informática e Investigación Operativ

Aplicación móvil para la identificación de variedades de Manihot esculenta Crantz cultivadas en Misiones mediante técnicas de deep learning

El cultivo de Manihot esculenta Crantz, conocida popularmente en la provincia de Misiones como mandioca, es muy importante ya que el aporte de hidratos de carbono de sus raíces complementa la canasta familiar, asimismo el requerimiento por parte de la industria de la raíz de la mandioca se incrementa día a día gracias al uso de la fécula de mandioca en la elaboración de productos aptos para celíacos. En Misiones se cultivan distintas variedades de mandioca, cada una de estas posee cualidades que la hacen aptas para distintos fines. A sim-ple vista las plantas de mandioca son similares para todas las variedades, pero los expertos del Instituto Nacional de Tecnología Agropecuaria de Misiones se en-cuentran trabajando en la generación de un registro único de variedades que sirva como guía para los productores. En este trabajo se plantea el uso de técnicas de deep learning para identificar mediante una imagen de la hoja o del tallo de una planta de mandioca la variedad correspondiente. Para esto se desarrolló una apli-cación móvil utilizando el modelo de aprendizaje por trasferencia MobileNet con Tensorflow, mediante la misma se logró clasificar de manera correcta el 92 % de las imágenes de tallos y el 81 % de imágenes de hojas de mandioca.Sociedad Argentina de Informática e Investigación Operativ

Aplicación móvil para la identificación de variedades de Manihot esculenta Crantz cultivadas en Misiones mediante técnicas de deep learning

El cultivo de Manihot esculenta Crantz, conocida popularmente en la provincia de Misiones como mandioca, es muy importante ya que el aporte de hidratos de carbono de sus raíces complementa la canasta familiar, asimismo el requerimiento por parte de la industria de la raíz de la mandioca se incrementa día a día gracias al uso de la fécula de mandioca en la elaboración de productos aptos para celíacos. En Misiones se cultivan distintas variedades de mandioca, cada una de estas posee cualidades que la hacen aptas para distintos fines. A sim-ple vista las plantas de mandioca son similares para todas las variedades, pero los expertos del Instituto Nacional de Tecnología Agropecuaria de Misiones se en-cuentran trabajando en la generación de un registro único de variedades que sirva como guía para los productores. En este trabajo se plantea el uso de técnicas de deep learning para identificar mediante una imagen de la hoja o del tallo de una planta de mandioca la variedad correspondiente. Para esto se desarrolló una apli-cación móvil utilizando el modelo de aprendizaje por trasferencia MobileNet con Tensorflow, mediante la misma se logró clasificar de manera correcta el 92 % de las imágenes de tallos y el 81 % de imágenes de hojas de mandioca.Sociedad Argentina de Informática e Investigación Operativ