Contents I. Introduction
As per the current population worldwide, primary food requirements are majorly accomplished by the use of plant. Disease occurred in plant affects the overall production loss. It can be overcome by non-stop tracking, monitoring with prevention methods. Early diagnosis of plant infection (P.I.) are assist by the use of computer imaginative and artificial intelligence (AI) to lessen the destructive outcomes of illnesses and although conquer the person steady watching. Being agricultural, India's financial system is largely based upon crop production. Agriculture is the spine of each financial system. The agriculture region is required to fulfil the need of food demand. The agriculture area needs a sizeable up-gradation to continue to exist the changing situations of the Indian financial system. for maximum yield and the crop must be healthy. Crop ailment is one of the essential elements which in a roundabout way have an impact on the vast reduction of each the first-class and quantity of agricultural products. There is a need for superior disorder identification in block infection to plants for the incidence of diseases in addition to maximizing the productiveness and making sure of agricultural sustainability. The world's population is expected to grow by 2 billion people by the year 2019 a significant increase of about 25% [1]–[2]. According to the Food and Agriculture Organization's (FAO) records, around 70-90% more food may be needed to feed this population. Nearly 16% of the world's total agricultural crop yield has been damaged by microbial diseases. In order to minimize damage to crops and reduce the spread of diseases, as well as to increase productivity and farming sustainability, better plant infection detection is required. Predicting plant illnesses in advance and preventing them makes more attention required for the researcher to increase the plant yield and reduce fungal, bacterial, etc., infection. Precision agriculture (PA) and plant phenotyping (PP) are modern methodologies with the extensive differences of improvements and identification of disease occurred in the plant as shown in Fig. 1 [3]. there's a certainly call for such methodologies inside the subject of agriculture for speedy and accurate plant disease detection. Precision agriculture is a technique wherein the feature of a crop is managed based totally on the spatial and brief parameters of the soil elements inside the area. This machine aims to achieve actual-time, strong mapping structures for crop, soil, and environment variables to facilitate a control choice as shown in Fig. 1. PP is the method this is the appearance and overall performance of a genotype of the plant underneath numerous environmental conditions. Especially, it is exertions some timeingesting and as such is as a substitute luxurious and the maximum of the PP technologies are imaged based totally. Phenotyping becomes come to trends frequently for non-invasive imaging and sensor-based evaluation of anatomical, physiological, and biochemical plant homes. Both methods have specific drawbacks that need to be addressed for high accurate plant ailment detection[4]. To deal with such issues new sensing-based totally methods ought to be explored and comprise the device inside the area for actual time plant sickness detection. Optical and MEMS sensors are promising detection techniques for non-invasive disorder detection and analysis. a brand new flexible primarily based sensor approach is of the brand new interest for the researchers as it aids to prepare a low-value plant sickness detection device. Fig. 2 shows the Multiple challenges faced by the plant due to food and environmental [5].
Precision agriculture (left) and plant phenotyping (right).
Depicts various difficulty faced as plant pathology
