SMART FARMING USING IMAGE PROCESSING AND IOT EDA GOWTHAM REDDY, D HEMALATHA, JAMI ABHINAY,MR. D.S. JOHN DEVA PRASANAAbstract” Due to the increasing demand in the agricultural industry, the need to effectively grow a plant and increase its yield is very important. In order to do so, it is important to monitor the plant during its growth period, as well as, at the time of harvest. In this image processing is used as a tool to monitor the diseases on leaves during farming, right from plantation to harvesting.
The attack of animals in the agricultural land and the theft of crops by humans cause heavy loss in cultivation and fruit ripeness detection along with deficiency of nutrients in the plant. In this work, we propose a hardware prototype using image processing.Keywords: image processing, fruit ripeness detection, disease identification.I. INTRODUCTIONAgriculture plays a dominant role in the overall economic scenario in India. Nowadays agriculture becomes very important due to the increasing population in the world.
Farmers are facing lot of problems in their cultivation areas, which include climatic change, water problem, pests and diseases. Expected crop loss is up to 37% every year. This project focuses mainly with the diseases. Due to the diseases caused to the plants leads to reduce the crop production. In recent years wild animals are special challenge for the farmers throughout the world, Animals like wild boars, elephant, tiger and monkeys etc., cause serious damage to crops by animals running over the field and trampling over the crops. It causes the financial problem to the farmers. Mango processing industry is one of the big fruit processing industries in the world. Ripening stage of various types of mangos can be estimated by visual inspection. The manual inspection is likely to vary from person to person and thus lack the uniformity and accuracy. It would also increase the time to market and manpower in the overall processing. The main idea of proposed system is addressing the above issues and increase the quality and quantity of the agriculture crop production. This project identifies the disease and intimate the farmers about the disease caused to the plants. To overcome from problem like animals enter into the field, this project uses the raspberry pi and camera to capture the images of the animals that are entering into the farm by pre-processing that images farmers will get these SMS containing area in which that animals observe. And also the animals repellent to the forest by using irritation noise by speaker and creating smoke by fogging machine. To identify the ripeness of mango fruit, this project avoid the manual process and introducing the automation would increase accuracy, effective use of mango fruits. Visual features play an important role in case of classification of mangos according to their ripening stage. Image processing can be applied to extract these features and analyse them to get the result. II. LITERATURE SURVEY1. LEAF DISEASE DETECTION USING IMAGE PROCESSINGThete Vaishali V, Thakare Pradnya R, Kadlag Gaurav B, P.A. Chaudhari…  This paper basically discusses about the Diseases in crops mostly on the leaves affects on the reduction of both quality and quantity of agricultural products. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases & quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes several steps viz. image acquisition, image pre-processing, segmentation, features extraction and neural network based classification.2. COLOR IMAGE SEGMENTATION FOR FRUIT RIPENESS DETECTIONMeenu Dadwal, V. K. Banga…  The main contribution of this paper is to represent different techniques to detect the rate of ripeness of fruits and vegetables. This paper reports techniques like histogram matching, clustering algorithms based image segmentation and relative value of parameter based segmentation. Each technique uses coloured images of fruits and vegetables as input data. In these techniques we set some threshold levels. By comparing the input data image with these threshold levels we can find the maturity level of given fruits and vegetables. 3. ANIMAL DETECTION SYSTEM IN FARM AREASVikhram B, Revathi B, Shanmugapriya R, Sowmiya S, Pragadeeswaran G  The main aim of our project is to protect the crops from damage caused by animal as well as divert the animal without any harm.Animal detection system is designed to detect the presence of animal and offer a warning. In this project we used PIR and ultrasonic sensors to detect the movement of the animal and send signal to the controller .It diverts the animal by producing sound and signal further, this signal is transmitted to GSM and which gives an alert to farmers and forest department immediately.4. DIAGNOSIS AND CLASSIFICATION OF GRAPE LEAF DISEASES USING NEURAL NETWORKSanjeev S Sannakki, Vijay S Rajpurohit, V B Nargund, Pallavi Kulkarni  Plant diseases cause significant damage and economic losses in crops. Subsequently, reduction in plant diseases by early diagnosis results in substantial improvement in quality of the product. Erroneous diagnosis of disease and its severity leads to inappropriate use of pesticides. The goal of proposed work is to diagnose the disease using image processing and artificial intelligence techniques on images of grape plant leaf. In the proposed system, grape leaf image with complex background is taken as input. Thresholding is deployed to mask green pixels and image is processed to remove noise using anisotropic diffusion. Then grape leaf disease segmentation is done using K-means clustering. The diseased portion from segmented images is identified. Best results were observed when Feed forward Back Propagation Neural Network was trained for classification.5. A SMART FARMLAND USING RASPBERRY PI CROP PREVENTATION AND ANIMAL INTRUSION DETECTION SYSTEMS Santhiya, Y Dhamodharan, N E Kavi Priya, C S Santhosh, M Surekha  This project is used to protect the farmland from animals by using Raspberry pi. Wild animals are special challenge for the farmers throughout the world. Animals like wild boars, elephants, monkeys etccause serious damage to crops. This project utilizes the RFID (Radio Frequency Identification Device) module and GSM (Global System Mobile) modem for this purpose. Forest officer and farmers will get these SMS containing area in which that animals observe. The techniques that already being used is ineffective, in this article we are presenting a practical procedure to ward them off, by creating a system which studies the behavior of the animal, detects the animal and creates the different sound that irritates the animal and also alerts the authorized person by sending a message. The animal can be detected by the RFID injector (for animals), the LF tag which inject under the animal skin. After the detection the intimation is sent. This project is mainly contributed to repellent the animals to the forest by using three stages are intimation, irritation noise and smoke by fog machine. III. EXISTING SYSTEMThere were different problems and challenges are identified in the existing system:1. In Existing method electric fences used to protect the crops from the wild animals. Due to electric fence animals are hurt widely and it is not only affects wild animals it also dangerous to the pet animals and even human beings.2. In Existing method ripen fruits can be picked manually. The manual inspection is likely to vary from person to person and thus lack the uniformity and accuracy. It would also increase the time to market and manpower in the overall processing. 3. In Existing method the manual classification and identification methods which are being used to distinguish between different types of leaf diseases are subjected to some kind of errors. Since these techniques are focused by human involvement so to avoid these problems the proposed system that could identifying diseases without any errors. IV. PROPOSED SYSTEMThe proposed system uses raspberry pi and camera to capture the images of the leaves and after pre-processing the captured image it provides information about the type of disease caused to the plant and also provides information about the deficiency of plants thereby saving time, money & power of the farmer. It also captures the images of fruits and then pre-processing the captured images it will provide information if the fruit is ripened. It also captures the images of field, if any animal enters into the field then sends a SMS to the farmers. The proposed system is used to reduce the man power and helps farmers to increase the productivity of crops.ADVANTAGES:1. Increase the productivity of crop.2. Detection of disease at initial stage.3. Reduce the human errors and man power.4. Reduce the damage to crops by animals running over the field and trampling over the crops. V. ARCHITECTURE DIAGRAM1. FRUIT RIPENESS DETECTION2. LEAF DISEASE IDENTIFICATION3. ANIMAL ATTACK OBSERVATIONVI. CONCLUSIONThe proposed project SMART FARMING serves as a reliable and efficient system and corrective action can be taken. The developed system is more efficient and saves a lot of time and beneficial for farmers. This system is reducing manpower therefore less energy of the farmer is required. In future this system can be improved by include mobile alerts and make the system fully automatic.VII. REFERENCES Prathibha S R 1, Anupama Hongal 2, Jyothi M P 3.,: IoT Basesd Monitoring System in Smart Agriculture, (2017).  K.A. Patil.,: A Model for Smart Agriculture Using IoT, (2016).  Thete Vaishali V 1, Thakare Pradnya R 2, Kadlag Gaurav B 3.,: Leaf Disease Detection Using Image Processing, (2017). Ramesh S 1, Vydeki D 2.,: Crop Disease Identification Using Embedded IoT System.  Meenu Dadwal,vk bangal.,color image segmentation for fruit ripness detection(2017). S. santhiya, y. dhamodharan, n e. kavi priya, c s. santhosh, m.surekha ,A smart farmland using raspberry pi crop prevention and animal intrusion detection system , 2018. Rahul Pralhad Salunkhel, Aniket Anil PatiF , Image Processing for Mango Ripening Stage Detection: RGB and HSV method ,2017.Shreya Lal,Santi Kumari Behera , Prabira Kumar Sethy, Identification and Counting of Mature Apple Fruit Based on BP Feed Forward Neural Network,2017.