A Detection Method of Rice Process Quality Based on the Color and BP Neural Network

Abstract. This paper proposed a detection method of rice process quality using the color and BP neural network. A rice process quality detection device based on computer vision technology was designed to get rice image, a circle of the radius R in the abdomen of the rice was determined as a color feature extraction area, and which was divided into five concentric sub-domains by the average area, the average color of each sub-region H was extraction as the color feature values described in the surface process quality of rice, and then the 5 color feature values as input values were imported to the BP neural network to detection the surface process quality of rice. The results show that the average accuracy of this method is 92.50% when it was used to detect 4 types of rice of different process quality

Classification Models for Plant Diseases Diagnosis: A Review

Plants are important source of our life. Crop production in a good figure and good quality is important to us. The diagnosis of a disease in a plant can be manual or automatic. But manual detection of disease in a plant is not always correct as sometimes it can be not be seen by naked eyes so an automatic method of detection of plant diseases should be there. It can make use of various artificial intelligence based or machine learning based methods. It is a tedious task as it needs to be identified in earlier stage so that it will not affect the entire crop. Disease affects all species of plant, both cultivated and wild. Plant disease occurrence and infection severity vary seasonally, regarding the environmental circumstances, the kinds of crops cultivated, and the existence of the pathogen. This review attempts to provide an exhaustive review of various plant diseases and its types, various methods to diagnose plant diseases and various classification models used so as to help researchers to identify the areas of scope where plant pathology can be improved

A brief study on rice diseases recognition and image classification: fusion deep belief network and S-particle swarm optimization algorithm

In the regions of southern Andhra Pradesh, rice brown spot, rice blast, and rice sheath blight have emerged as the most prevalent diseases. The goal of this research is to increase the precision and effectiveness of disease diagnosis by proposing a framework for the automated recognition and classification of rice diseases. Therefore, this work proposes a hybrid approach with multiple stages. Initially, the region of interest (ROI) is extracted from the dataset and test images. Then, the multiple features are extracted, such as color-moment-based features, grey-level cooccurrence matrix (GLCM)-based texture, and shape features. Then, the S-particle swarm optimization (SPSO) model selects the best features from the extracted features. Moreover, the deep belief network (DBN) model trained by SPSO is based on optimal features, which classify the different types of rice diseases. The SPSO algorithm also optimized the losses generated in the DBN model. The suggested model achieves a hit rate of 94.85% and an accuracy of 97.48% with the 10-fold cross-validation approach. The traditional machine learning (ML) model is significantly less accurate than the area under the receiver operating characteristic curve (AUC), which has an accuracy of 97.48%

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed

PDCNN: FRAMEWORK for Potato Diseases Classification Based on Feed Foreword Neural Network

           يعتمد الاقتصاد بشكل استثنائي على الإنتاجية الزراعية. لذلك ، في مجال الزراعة ، يعد اكتشاف عدوى النبات مهمة حيوية لأنه يعطي تقدماً واعداً نحو تطوير الزراعة. في هذا العمل، تم اقتراح نظام لتصنيف أمراض البطاطا بالاعتماد على الشبكة العصبية. يهدف النظام إلى كشف وتصنيف أربعة أنواع من أمراض درنات البطاطا وهي: النقطة السوداء ، الجرب الشائع، فيروس البطاطا Y واللفحة المبكرة بالاعتماد على صورهم. يتكون النظام من ثلاثة مستويات: مستوى المعالجة المسبقة هو المستوى الاول، والذي يعتمد على K-means clustering  لاكتشاف المنطقة المصابة من صورة البطاطا، المستوى الثاني هو مستوى استخراج الميزات والذي يستخرج الميزات من المنطقة المصابة بالاعتماد على طريقتين:  grey level run  length matrix  و  first order histogram based features. الميزات المستخرجة من المستوى الثاني تستخدم في المستوى الثالث في تغذية الشبكة العصبية الأمامية لإجراء عملية التصنيف . 120 صورة ملونة استخدمت, 80 صورة استخدمت في تدريب الشبكة و40 صورة استخدمت في عملية الاختبار . النظام المقترح فعال للغاية في تصنيف أربعة أنواع من أمراض درنات البطاطا وكانت نسبة التمييز 91,3 %  .         The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The second level is features extraction which extracts features from the infected area based on hybrid features: grey level run length matrix and 1st order histogram based features. The attributes that extracted from second level are utilized in third level using FFNN to perform the classification process. The proposed framework is applied to database with different backgrounds, totally 120 color potato images, (80) samples used in training the network and the rest samples (40) used for testing. The proposed PDCNN framework is very effective in classifying four types of potato tubers diseases with 91.3% of efficiency

Electronic noses and tongues to assess food authenticity and adulteration

[EN] Background: There is a growing concern for the problem of food authenticity assessment (and hence the detection of food adulteration), since it cheats the consumer and can pose serious risk to health in some instances. Unfortunately, food safety/integrity incidents occur with worrying regularity, and therefore there is clearly a need for the development of new analytical techniques. Scope and approach: In this review, after briefly commenting the principles behind the design of electronic noses and electronic tongues, the most relevant contributions of these sensor systems in food adulteration control and authenticity assessment over the past ten years are discussed. It is also remarked that future developments in the utilization of advanced sensors arrays will lead to superior electronic senses with more capabilities, thus making the authenticity and falsification assessment of food products a faster and more reliable process. Key findings and conclusions: The use of both types of e-devices in this field has been steadily increasing along the present century, mainly due to the fact that their efficiency has been significantly improved as important developments are taking place in the area of data handling and multivariate data analysis methods. (C) 2016 Elsevier Ltd. All rights reserved.Peris Tortajada, M.; Escuder Gilabert, L. (2016). Electronic noses and tongues to assess food authenticity and adulteration. Trends in Food Science and Technology. 58:40-54. doi:10.106/j.tifs.2016.10.014S40545

Machine vision detection of pests, diseases, and weeds: A review

Most of mankind’s living and workspace have been or going to be blended with smart technologies like the Internet of Things. The industrial domain has embraced automation technology, but agriculture automation is still in its infancy since the espousal has high investment costs and little commercialization of innovative technologies due to reliability issues. Machine vision is a potential technique for surveillance of crop health which can pinpoint the geolocation of crop stress in the field. Early statistics on crop health can hasten prevention strategies such as pesticide, fungicide applications to reduce the pollution impact on water, soil, and air ecosystems. This paper condenses the proposed machine vision relate research literature in agriculture to date to explore various pests, diseases, and weeds detection mechanisms

Numerical Modeling and Design of Machine Learning Based Paddy Leaf Disease Detection System for Agricultural Applications

In order to satisfy the insatiable need for ever more bountiful harvests on the global market, the majority of countries deploy cutting-edge technologies to increase agricultural output. Only the most cutting-edge technologies can ensure an appropriate pace of food production. Abiotic stress factors that can affect plants at any stage of development include insects, diseases, drought, nutrient deficiencies, and weeds. On the amount and quality of agricultural production, this has a minimal effect. Identification of plant diseases is therefore essential but challenging and complicated. Paddy leaves must thus be closely watched in order to assess their health and look for disease symptoms. The productivity and production of the post-harvest period are significantly impacted by these illnesses. To gauge the severity of plant disease in the past, only visual examination (bare eye observation) methods have been employed. The skill of the analyst doing this analysis is essential to the caliber of the outcomes. Due to the large growing area and need for ongoing human monitoring, visual crop inspection takes a long time. Therefore, a system is required to replace human inspection. In order to identify the kind and severity of plant disease, image processing techniques are used in agriculture. This dissertation goes into great length regarding the many ailments that may be detected in rice fields using image processing. Identification and classification of the four rice plant diseases bacterial blight, sheath rot, blast, and brown spot are important to enhance yield. The other communicable diseases, such as stem rot, leaf scald, red stripe, and false smut, are not discussed in this paper. Despite the increased accuracy they offer, the categorization and optimization strategies utilized in this work lead it to take longer than typical to finish. It was evident that employing SVM techniques enabled superior performance results, but at a cost of substantial effort. K-means clustering is used in this paper segmentation process, which makes figuring out the cluster size, or K-value, more challenging. This clustering method operates best when used with images that are comparable in size and brightness. However, when the images have complicated sizes and intensity values, clustering is not particularly effective

Long Short-Term Memory Recurrent Neural Networks for Plant disease Identification

Farming profitability is something on which economy profoundly depends. This is the one reason that sickness recognition in plants assumes a critical job in farming field, as having infection in plants are very common. In the event that legitimate consideration isn't taken here, it causes genuine consequences for plants and because of which particular item quality, amount or profitability is influenced. This paper displays an algorithm for image segmentation technique which is utilized for automatic identification and classification plant leaf infections. It additionally covers review on various classification techniques that can be utilized for plant leaf ailment discovery. As the infected regions vary in length it is difficult to develop a feature vector of identical finite length representing all the sequences. A simple method to go around this issue is given by Recurrent Neural Networks (RNN). In this work we separate a feature vector through the use of Long Short-Term Memory (LSTM) recurrent neural networks. The LSTM network recursively repeats and concentrates two limited vectors whose link yields finite length vector portrayal

An edge texture features based methodology for bulk paddy variety recognition

The paper presents a method for recognition of paddy varieties from their bulk grain sample edge images based on Haralick texture features extracted from grey level co-occurrence matrices. The edge images were obtained using Canny and maximum gradient edge detection methods. The average paddy variety recognition performances of the two categories of edge images were evaluated and compared. A feature set of thirteen texture features was considered and the feature set was reduced based on contribution of each feature to the paddy variety recognition accuracy. The average paddy variety recognition accuracy of 87.80% was obtained for the reduced eight texture features extracted from maximum gradient edge images. The work is useful in developing a machine vision system for agriculture produce market and developing multimedia applications in agriculture sciences