India is an agricultural country and most of the people, wherein about 70% depends on agriculture. So, disease detection in plants is very important. Tomato is one of the strongly grown and widely used crops. There are many types of tomato diseases and pests, in which the pathology of which is complex. Crop diseases are a major threat to crop production, but their identification remains difficult in many parts of India due to the lack of the necessary infrastructure. It is very prone for attacks by aphids, whiteflies, Thrips. It is difficult and error-prone to simply rely on manual identification in a large open area. Recent advances in computer vision made possible by deep learning has made the way for automatic disease detection. To monitor the health of the tomato crops in acres of land where we cannot monitor the output of each sensor individually, AI is used to increase the yield and quality of crops using a Convolution Neural Networks (CNN), k-means clustering, and acoustic emission.

INTRODUCTION

India has vast area, but the current status of agriculture management is not sufficient to provide everything to the population, which can be problematic. The solution to this issue is the practice of monitoring and protecting the crops in the open land farming. Automation system is the technical approach in which the farmers in the rural areas are benefited by automatic monitoring and controlling of pests and protecting the crops. It replaces the direct supervision of the human. Here, AI is used to monitor the health of the tomato crops in large acres of land where we cannot monitor the output of each sensor individually with the help of Convolution Neural Networks and K means clustering and thus increasing the yield and quality of the crops.

The development and growth of crop depends on the temperature and humidity. The controlling and monitoring of open land parameters play vital role in overall development of plant. The objective of our project is to design a simple, efficient “Arduino‟ based system for automation of open land. The project features monitoring, recording and controlling the values of temperature, humidity and soil moisture inside the open land. The Arduino used is a highly compact, durable and easily available. The values of temperature and soil moisture are continuously communicated through various sensors to the Arduino. Also proper design, selection, construction and the management of the open land using sensors would augur well to the growth of a crop.

LITERATURE SURVEY

Susperrangi, Carlos Rubio and Libor Lenza presented the development and comparison of two different approaches for vision based automated pest detection and identification using learning strategies [1]. Santhosh Adhikari and Er.Saban Kumar presented the classification and detection of the plant’s diseases automatically especially for the tomato plants [2]. Christina Mueller Blenkle and Sascha Kirchner designed a system in which the position and sound of hidden insects are also detected, but the settlement sound of grain can be mistaken for insect sound [3]. K.Narsimha Reddy, B.Polaiah and N.Madhu presented the overview of different classification techniques [4]. Preetha Rajan, Radhakrishnan B presented the study of various image processing techniques and applications for pest identification and plant disease detection [5].

PROPOSED WORK

Owing to the inaccurate prediction results which is obtained from use of thermal and image sensor we propose to introduce acoustic sensors. This system is basically proposed only for large areas in acres to increase the crop yield without getting affected by pests. The system is trained to read the sensor output and the prediction is done through machine learning which improves the accuracy. The use of AI reduces the work of labors.

a) Arduino Uno

Arduino is a single-board microcontroller, intended to make the application of interactive items or environment further useful. It involves the whole lot to support the microcontroller; without problems connect it to a laptop with a USB cable or power it with an ac to dc adapter or battery to get began out. The Uno differs from all previous boards in that it does no longer use the FTDI USB to- serial using drive. It has 14 digital input/output pins (of which 6 can be used as PWM outputs), 6 analog inputs, a USB connection, a vigor jack, a reset button and more. It includes everything needed to aid the microcontroller; conveniently join it to a laptop with a USB cable or vigor it with a AC-to-DC adapter or battery to get started.

b) Acoustic Sensor

Literally acoustic or sound sensor is used to detect the sound. It is a small board that combines a microphone and som