Investigation of Deep Learning Models for Disease Prediction in Wheat Plant Leaves

Abstract

Plant or crop diseases have a significant impact on agricultural productivity and quality. newlineThe wheat industry plays a significant role in the agricultural economy of India. Identifying newlineof leaf diseases is essential to increasing crop production and growth. For the most part, newlinefarmers and other experts use their own eyes to examine plants for symptoms of disease. It newlineis, however, a time-consuming and expensive method, as well as inaccurate. Wheat rust newlinediseases are the oldest plant disease known to humans. There are several leaf diseases newlineresponsible for the damaging the crop and leaf rust is one of the major diseases which can newlinedestroy the entire wheat crop. Leaf diseases in crops can be predicted more quickly and newlineaccurately, which could lead to the development of an early treatment method and a newlinesignificant reduction in economic losses. Therefore, an automatic wheat leaf diseases newlinedetection system is required which detects the wheat disease. Advancement in computer newlinevision technologies has offered new dimensions in the direction of developing a decision newlinesupport system that can assist in identification and detection of plant diseases in early stage. newlineThe use of computer vision and pattern recognition to detect disease has been studied in an newlineeffort to reduce losses and achieve intelligent healthy farming. The image recognizing newlinetechniques are quite useful for automatic detection of diseases quickly and accurately. newlineIn the field of agricultural information, the automatic detection and diagnosis of plant newlinediseases is highly desired. Many approaches have been put forth to tackle this problem, but newlinedeep learning is quickly gaining favor due to its superior performance. Several recent newlinestudies have proposed that modern automatic image recognition systems based on deep newlinelearning improve existing procedures for early detection of plant diseases. The use of deep newlinelearning for image classification is quickly becoming the norm. A major scientific newlinechallenge is to use Deep Learning for accurate classification of small datasets. In addition, newline