Identification of rice diseases using deep convolutional neural networks

Identification of the plant diseases is the key to preventing the losses in the yield and quantity of the agricultural product. The studies of the plant diseases mean the studies of visually observable patterns seen on the plant. Health monitoring and disease detection on plant is very critical for sustainable agriculture. It is very difficult to monitor the plant diseases manually. It requires tremendous amount of work, expertize in the plant diseases, and also require the excessive processing time. Hence, image processing is used for the detection of plant diseases. Disease detection involves the steps like image acquisition, image pre-processing, image segmentation, feature extraction and classification. This paper discussed the methods used for the detection of plant diseases using their leaves images. This paper also discussed some segmentation and feature extraction algorithm used in the plant disease detection.

Plant Disease Detection Using Image Processing

A review on the main challenges in automatic plant disease identification based on visible range images

This paper presents a survey on methods that use digital image processing techniques to detect, quantify and classify plant diseases from digital images in the visible spectrum. Although disease symptoms can manifest in any part of the plant, only methods that explore visible symptoms in leaves and stems were considered. This was done for two main reasons: to limit the length of the paper and because methods dealing with roots, seeds and fruits have some peculiarities that would warrant a specific survey. The selected proposals are divided into three classes according to their objective: detection, severity quantification, and classification. Each of those classes, in turn, are subdivided according to the main technical solution used in the algorithm. This paper is expected to be useful to researchers working both on vegetable pathology and pattern recognition, providing a comprehensive and accessible overview of this important field of research.

/pdf/digital-image-processing-techniques-for-detecting-19gzwksbx2.pdf

Digital image processing techniques for detecting, quantifying and classifying plant diseases.

Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.

Applications of Remote Sensing in Precision Agriculture: A Review

The techniques of machine vision are extensively applied to agricultural science, and it has great perspective especially in the plant protection field, which ultimately leads to crops management. The paper describes a software prototype system for rice disease detection based on the infected images of various rice plants. Images of the infected rice plants are captured by digital camera and processed using image growing, image segmentation techniques to detect infected parts of the plants. Then the infected part of the leaf has been used for the classification purpose using neural network. The methods evolved in this system are both image processing and soft computing technique applied on number of diseased rice plants.

Rice disease identification using pattern recognition techniques

Rice diseases classification using feature selection and rule generation techniques

Multi-objective optimization technique for resource allocation and task scheduling in vehicular cloud architecture

Background The socio-economic status of the countries is ever-changing due to the increasing population. 'Green Revolution', a paradigm shift towards technology driven agriculture, has ensured a good quality of life to some extent for this increasing population. However, it brings in several negative impacts on the environment, which poses a threat to the sustainability of agriculture and natural resources. Precision agriculture (PA) is a state-of-the-art concept of site-specific farm management that helps to overcome this threat in a smart way using modern information and communication technologies. It reduces the indecorous use of resources, pollution and hence improves quality of life, which in turn helps to achieve sustainable development goals. Results The objective of this systematic review is to understand the present status, benefits, and limitations of the state-of-the-art technologies used in PA. A total 67 articles are identified following the PRISMA guideline to inspect the technical innovations at different components of PA. The articles are examined based on the novelties, measured parameters, technologies, and field of applications. Conclusion This analysis results in a significant understanding about the present knowledge gap and identification of the potential future research opportunities for sustainable agronomy. © 2019 Society of Chemical Industry.

State-of-the-art technologies in precision agriculture: a systematic review.

Voice activity detection (VAD) refers to the task of identifying vocal segments from an audio clip. It helps in reducing the computational overhead as well elevate the recognition performance of speech-based systems by helping to discard the non vocal portions from an input signal. In this paper, a VAD technique is presented that uses line spectral frequency-based statistical features namely LSF-S coupled with extreme learning-based classification. The experiments were performed on a database of more than 350 h consisting of data from multifarious sources. We have obtained an encouraging overall accuracy of 99.43%.

Santanu Phadikar

Papers

Rice disease identification using pattern recognition techniques

Rice diseases classification using feature selection and rule generation techniques

Multi-objective optimization technique for resource allocation and task scheduling in vehicular cloud architecture

State-of-the-art technologies in precision agriculture: a systematic review.

Line spectral frequency-based features and extreme learning machine for voice activity detection from audio signal