DETECTION OF ANTHRACNOSE DISEASE IN CHILI USING IOT AND FIELD DATA

— Internet of thing IoT introduced various opportunities for novel applications, which are utilized in agriculture and many more. In the world, Chilli is very significant plant due to its huge ingesting. Chilli has many medicinal properties and using in many foods in Pakistan. Anthracnose produced through Colletotrichum spp. consumes stayed one of the greatest vital viruses of Chilli and in worldwide which can cause in crop victims of up to 50%. Chilli can also decrease the danger of tumor by avoiding chemicals from mandatory to chromosome and decrease calorie consumption through growing thermogenesis. Traditionally, the findings and usage of the insecticide are more frequently complete in the ﬁelds but this procedure is more time-intense, risky, and not accurate method in most of the time. The main focus of this effort is to grow a system to detect disease detection using internet of thing IoT on the bases of epidemiology of anthracnose disease in chill to Enhancement of production. Controller collect data from field sensors and send to cloud sever and use of k-nearest neighbor (KNN) classifier for analysis the accurate results. The research is very important in terms to increase the production of agriculture system in Pakistan. The system will identify the diseases in the earlier time and categorize anthracnose disease and send information to the farmers to safe their crops.


INTRODUCTION
Chilli cultivated in all over the world and a common crop. Chilli is raised up an area of 1776 thousand in the world, with a productivity of 7182 thousand tons According the agriculture information. Chilli is well-known as parsimoniously very valuable cash crop in Pakistan. It belongs to family Solanaceae, as are potatoes, tomatoes and egg plants. (Abid, 2014) And is considered valuable cash crop around the world and also in Pakistan. In Pakistan during 2013, area under pepper Chilli cultivation was 66500 hectares with a total production of 23077 tons (FAOSTAT, 2013). Anthracnose produced through Colletotrichum spp. is most serious threat of crops in the tropical and subtropical places of worldwide. According to the scientific and economic status, Colletotrichum was the eighth maximum hazardous collection of plant pathogenic molds in the sphere's crop (Dean R, 2012).
In these crowds, Chilli pepper (Capsicum spp.), an valuable financial yield worldwide (JM, 1992), that is seriously attacked by chili disease called anthracnose which may result in yield damages of up to 50% (Pakdeevaraporn P, 2005) Chilli is a central vegetable because of its huge depletion worldwide. It's uses in many foods usually and it is also utilized in medical field. Moreover, chili can decrease the threat of cancer by stopping chemicals from binding to DNA and decrease calorie consumption by growing thermogenesis (Sahitya, 2014). The chili disease anthracnose due to Colletotrichum species extremely increase the excellence and crop of chili fruits causing in low earnings to farmers.
The traditional method of diagnostic and use of the chemical and pesticides are more often done in the fields to avoid a huge loss. It was the more time-consuming, risky, and incorrect method in most of the time with unnecessary practices of the pesticides (J. Ma, Nov. 2018). Another major problem is that Epidemiology is the reading of outburst of a transferable illness. The key task of this effort is to invent a structure to detect early disease detection using internet of thing to Enhancement of chili production. Use some sensors called temperature and humidity sensor and soil ph sensor. Use a microcontroller called Arduino to collect data from sensors. The IoT is support becoming popular day by day in the today's world, around 5 billion items have communicate with the use of internet. Internet of thing (IoT) introduced a lot of techniques and opportunities in home monitoring, healthcare system, agriculture department and many more. KNN classifier use to calculate the nearest disease values from collect data by sensors. In this project, the detection as wells the remedy for curing it is achieved.

II. LITERATURE REVIEW
In this purposed method, preparing a picture with any perceptual space isn't the most proficient strategy; every sort of picture perceptual space needs to choose. For this situation, HSI shading space was the best to recognize the ailment. A key advance is the change between RGB spaces and perceptual spaces generally utilized (HSL, HSI and HSV) and the approach for picture division, for example, edge and Prewitt channels (J. L. González-Pérez, 2013 ) In this examination proposes that picture handling investigation is a proficient, compelling and fast strategy for illness discovery in the beginning phases. The outcomes likewise associated with color substance and affirmed that C. gloeosporioides contamination is available in the greeneries, harms the sprig chlorophyll, limits photosynthesis and eventually brings about the passing of the greeneries. It is the main investigation where IP and svm are utilized as compelling apparatuses for anthracnose illness location. We presented various calculations for computing malady seriousness by taking a straightforward picture (Srinivasan, 2018) In the presented study, by supervisory the biotic components

Epidemiology of disease anthracnose in Chilli
Environmental factors play a vibrant role to decide the formation and diffusion of illness. The satisfactory swarm, pathogen and climate circumstances hint to formation of illness (Agrios, 2005). Summer and wet ecological circumstances support the spread of the disease. Temperature about 27•C with comparative moisture of 80% and mud pH of 5-6 have described to be the best favorable circumstances for effective formation of the illness in an assumed part (Roberts, 2001).

Role of IoT in Agriculture
Smart farming based on IoT technologies become very popular for the farmers to reduce the time-consumption and promote the quality and productivity of agriculture. IoT smart farming system work with some sensors and devices to monitor the field condition. The agriculturalists can screen the field circumstances from wherever and they can take better decision on time for enhancement in production of agriculture.

DHT-11 Humidity and Temperature Sensor
The DHT11 is named as temperature and Moisture instrument.
The entire quantity of aquatic gas in air is defined as an amount of moisture. When there is an alteration in temperature, comparative moisture also reformed. The quantity of liquid dews in air is enlarged after irrigation. This reasons reduction in temperature which in go upturns the comparative moisture of the surrounds. The temperature and moisture analysis are frequently alerted to the operator so that the operator can be capable to identify the field situations from wherever (Chinmay Tadwalkar, 2019). Temperature nearby 27•C with relation moistness of 80% take testified to remain the utmost finest situations for efficacious establishing of the ailment in an assumed region (Roberts, 2001).In this arrangement DHT11 is coupled to the controller board called ardiuno by connectors.
The resulting yield is sent to the edge node. This output is aggregated and forwarded to cloud system for storing.   DHT11 device has three bits and it's built-in on a panel. If yours has four bits, formerly you want to construct this circuit after the device.

Fig .6 Configuration of Arduino Uno and DHT11 sensor
The system design The general process of forming the structural design, components, crossing points, and fact for a structure to suit precise necessities is called Systems design. Systems design could be seen as the use of hypothesis of the systems to product development. Fig. depicts  where N0 is the arrangement of k -nearest perceptions and I(yi=j) is a pointer variable that assesses to 1 if a given perception (xi,yi) in N0 is an individual from class j , and 0 assuming in any case. Subsequent to assessing these probabilities, k -nearest neighbors relegates the perception x0 to the class which the past likelihood is the best.

V. DISCUSSION
Include the Arduino controller in the system to collected data from all sensors. The cloud server consist some trained algorithms on obtainable data. The cloud process the data and match the values and then show then result. The possibility that the accuracy of the results will be over eighty. That is the generated output should be correct at least eighty. Then the result alert should be sent to farmers for making them aware of the disease

VI. CONCLUSION
The main focus of this work on Chilli diseases anthracnose detection system. This work done by implementation in real time and generate accuracy in results. The research is an important source to enhance the productivity of agriculture in Pakistan. The technical developing system will be helpful for 80 the farmers to safe their crops from huge losses. Finally achieve that system will identify the diseases in the earlier time and categorize anthracnose disease and send information to the farmers for safe their crops.

ACKNOWLEDGMENT
Firstly I would like to express thank Allah Almighty aimed at giving us strength to complete this research. We would like to thank our gratitude to our teacher Dr Hameedur Rahman for his useful comments, remarks and engagement through the process of our research. Without the help of our teacher this research would not been possible.