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Plant diseases are a huge threat to agriculture throughout the world. The use of non-contact technologies for identifying and monitoring plant and pest illnesses across wide geographical areas, as well as their high efficiency and low cost, could significantly improve plant protection efforts. It has been created in a variety of methods a number of remote sensing approaches for the identification and monitoring of plant diseases and pests. This research summarizes state-of-the-art research achievements in sensing technologies, feature extraction and monitoring, and algorithms of various sizes that have been made in recent years. Detection and monitoring of plant diseases necessitates the use of sensing systems that are classified based on their characteristics and maturity. On the basis of the data collected by these remote sensing devices and the sensitivity analysis performed during the detection and monitoring process, a range of remote sensing functions are proposed and identified as surrogates for the original functions. A further area of investigation is the development of algorithms that correlate remote sensing properties with the presence of plant diseases and pests in order to detect, identify, and determine the severity of diseases and pests across broad regions of land. When it comes to agriculture in tropical and subtropical regions, sugarcane is the most important crop. Saccharum Officinarum (sugarcane) cultivation has been practiced in India since the Vedic era, and the country is now generally recognized as the source of the sweetener. Sugarcane, in the form of sucrose or sugar, is used to manufacture the vast majority of food products. The poaceae grass family is farmed all over the world, with Brazil and India being the two largest producers. Health of the plant has an impact on the production of its products such as jaggery, sugar, molasses and other sugar-based goods, among other things. India is the world's second-largest producer of sugar cane, trailing only Brazil, and the Indian sugar industry provides employment opportunities for both qualified and semi-skilled individuals. Sugar is a critical food item for both the customer and the government in terms of revenue generation and distribution. It makes a significant contribution to the socio-economic development of the country. Sugar cane is the raw material used in the production of white sugar, jaggery (gur), ethanol, and bagasse, which are all produced in biogas power plants and paper mills. Brazil is a major producer of sugarcane on a global scale. BoengPokkah is a fungal illness caused by the Fujikuroi species complex (FFSC), which is one of the most important fungal infections in the world 1. Sugarcane consumption also has the added benefit of protecting us against prostate and breast cancer. Blood pressure can also be regulated with the use of acupuncture.
A key thread in agricultural output in recent years has been plant diseases, which have had a considerable influence on farmers' incomes and have continued to be a drag on the Indian economy as a result of their occurrence. Plant ailments are discovered and diagnosed earlier, allowing for more effective treatment and management of the disease. It is possible to identify and diagnose plant diseases using a wide variety of approaches. Estimation costs are prohibitively expensive, and virtual human evaluations are inefficient, which is impeding the rapid expansion of modernized agriculture. Precision-based agriculture, in conjunction with the implementation of digital monitoring and the modernization of computer technology, strongly advises the use of an automated disease detection technique. The proposed research, which is based on a deep learning approach for the detection of pictorial plant illnesses, includes deep learning models for the automatic diagnosis of sugar cane plant diseases using pictorial images. The training of these networks takes time, but once they're trained, they can make it more feasible for farmers to detect plant disease using a sugarcane plant sheet dataset in real time with plant village dataset, which is beneficial for both farmers and researchers.