Plant leaf disease detection and control: A survey. Issue 2 (17th February 2020)

• Record Type: Journal Article
• Authors: Joshi, Barkha M.; Bhavsar, Hetal
• Is Part Of: Journal of information & optimization sciences. Volume 41:Issue 2(2020)
• Page Start: 475
• Page End: 487
• Publication Date: 2020-02-17
• Subjects: 68T30; Plant disease -- Disease identification -- Plant leaf Symptoms -- Treatment policy; Electronic data processing -- Periodicals; Information science -- Periodicals; Mathematical optimization -- Periodicals; 519.6
• DOI: 10.1080/02522667.2020.1734295
• Languages: English
• ISSNs: 0252-2667

Abstract

Abstract: The most important aspect of the Indian economy is agriculture. It is very common and natural to have a disease in plants with varying climatic conditions. This further leads to the crop quality getting deteriorated. With the recent changes in the weather cycles, achieving the best quality and quantity of crop is the most challenging task for farmers. With such challenges at the forefront, image processing has been proved as the best technique to detect the initial stages of disease based on the color, texture, and shape of the crop leaf. There are many different feature extraction techniques like color histogram, canny and Sobel edge detector, gray level co-occurrence matrix, Gabor filter which are used for extracting the feature of the disease in a crop leaf. Once disease features are extracted then classification algorithms like Support Vector Machine, Artificial Neural Network, Backpropagation, Convolution Neural Network, Feed Forward Neural Network, Probabilistic Neural Network, and Radial Neural Network are used to classify the disease. Once disease reason is identified, then proper treatment can be applied after identifying the reason behind the disease. These image processing and classification techniques have been proved accurate but feature extraction is the most time-consuming method as it is done manually using the different methods. Also, image processing with classification works for the small dataset. Convolutional Neural Networks as a part of deep learning, and it's a most effective sub branch of image processing. Many applications for the automatic identification of plant leaf diseases have been developed. These applications could serve as a basis for the development of expert assistance. Such types of tools could contribute great and sustainable agricultural practices and greater food production security. To examine the potential of these networks for such applications, we survey research studies that relied on CNNs to automatically identify plant leaf diseases. We describe their performance and main implementation aspects. Our survey allows us to identify the issues in this research area. This work covers the survey of different types of the disease occur in plants, its causes, symptoms, and treatment policy with the image processing and classification methods used. This survey also discussed the limitations of existing research work which provides the direction for further improvements in plant disease identification and preventive measures.