LITERATURE SURVEY

SRI  VENKATESHWARA COLLEGE OF ENGINEERING

Vidyanagar, Bengaluru - 562157 

Department of Electrical and Electronics Engineering

FUZZY LOGIC BASED AUTOMATED SEED SOWING ROBOT

PRESENTED BY:                                                             GUIDE:              

ANUSHA K                                                    Dr. Vijayashree R Budyal       

ACHUTH KUMAR V                            HOD Dept of EEE SVCE Bengalur       

PRAJWAL KUMAR J

SAHANA BR

The literature survey conducted on the project is as given below and the limitations found in all these paper is also mentioned 

Masood Ul Hassan, Mukhtar Ullah, Jamshed Iqbal [1] Towards autonomy in agriculture: Design and prototyping of a robotic vehicle with seed proposes the design details of the robot which is responsible for the selection of a single seed based on the quality of the seed.The only limitation with the project is the robotic vehicle doesn’t run automatically.

Neha S. Naik, Virendra  V Shete,  Shruti. R. Danve [2] Precision based agriculture robot suggests a precision agriculture method which enables the farmer to sow the seed at optimal depth and optimal distance between the crops and rows with respect to different crops. It possess a limitation of selection of viable seeds which is only done but not based on the soil.

Saurabh Umarkar; Anil Karwankar [3] Arduino Based Automated seed sowing Robot  suggests the seed sowing and digging mechanism of operation for different fields but it comes with a limitation which is not adaptable to different soil textures.

T. Tessier and W. Kinsner[4] The paper [4] proposes the use of Fuzzy Logic for determining the error between the measured seeding depth and the desired depth set point and manipulating it in such a way that error is red. To test the strategy, the planned activity to be performed was planting seeds, in a field composed by a grid of points that represents the places to be sown. Completion times and collision avoidance attempts were measured to test the effectiveness, as well as to perform a comparison between the distributed and centralized methodsuced and the system is more efficient but the drawback is, it is only applicable for depth monitoring based on the spoil textures.

W.Kinser ,G Gamby ,R.A. Froese and T.Tessier[5] Fuzzy seeding depth monitoring system uses Fourier Transformation allow the Nyquist Sampling and adjustment period.

Santhi, P. V., Kapileswar, N., Chenchela, V. K. R., & Prasad, C. H. V. S. [6]. Sensor and vision based autonomous AGRIBOT for sowing seeds. Sensor and vision based autonomous agribot for sowing seeds successfully designed and fabricated, the errors and inaccuracies have been eliminated and calibration is done. The suspension system is tested and found that it is able to handle bumps up to 3cm. We have extracted the edges from the acquired image. A lead screw mechanism along with ultrasonic sensor and IR sensor were used to sow the seed at an optimal depth for proper germination of the plant.

Jayakrishna, P. V. S., Reddy, M. S., Sai, N. J., Susheel, N., & Peeyush, K. P. [7] Autonomous Seed Sowing Agricultural Robot. A four wheel drive robot that does the work of seed sowing in ploughed agricultural land avoiding the human effort by tracing the path and sowing seeds at equal intervals using the field area parameters(length and breadth) and seed spacing intervals as inputs specified by the user.

Gollakota, A., & Srinivas, M. B. [8]. Agribot — A multipurpose agricultural robot. It is designed to minimize the labor of farmers in addition to increasing the speed and accuracy of the work. It performs the elementary functions involved in farming i.e. ploughing the field, sowing of seeds and covering the seeds with soil. The robot is autonomous and provides the facility for optional switching of the ploughing system when required.

Srivastava, A., Vijay, S., Negi, A., Shrivastava, P., & Singh, A.[9] DTMF based intelligent farming robotic vehicle: An ease to farmers proposes the design of a robot which is controlled by a cell phone, making  machine to  communicate on a large scale over a large distance  helping  the farmer to control his agricultural works from a far distance without going in the field with an easy control.

Lucas, P., Loayza, K., & Pelaez, E. [10] A Distributed Control of Movements and Fuzzy Logic-Based Task Allocation for a Swarm of Autonomous Agents proposes a decentralized method for controlling a swarm of autonomous agents represented as Unmanned Aerial Vehicles (UAVs) to accomplish a set of tasks cooperatively. The task commissioning is carried out by a Fuzzy Logic-based task allocation strategy, and the movement behavior is based on the internal state of the agent as well as the external data from its neighbors through local communications.

Swanand Sinalkar ; Gayatri Phade[11]Treek'lam suggests a design to construct a drone that can plant seeds. The drone will be equipped with a drilling tool, seed-sowing mechanism, sensors for robot protection, a communication module and a controlling module. The system will perform the operation for sowing the seeds in the area defined by the operator. The paper introduces the use of robots in tree plantation that ultimately results in saving of human effort, time and cost. It also brings uniformity in the structural pattern of tree plantation.

Konam, S., N., N. S. R., & K., M. K.[12]. Design Encompassing Mechanical Aspects of ROTAAI: RObot to Aid Agricultural Industry introduces ROTAAI (Robot to aid agricultural Industry), a multi-faceted Agribot that could analyze the terrain, cut the mangoes and avoid animal intrusion in addition to already established seed-sowing, fertilizer spraying, manure spreading and watering the farms. . Considering the sheer amount of intense modelling, extensive circuitry and complex image processing algorithms required to describe the functioning of Agribot,

Patel, N. R., Kale, P. D., Raut, G. N., Choudhari, P. G., Patel, N. R., & Bherani, A. [13]. Smart design of microcontroller based monitoring system for agriculture. The developed system monitored different agricultural parameters and transmitted them wirelessly to central unit via. Microcontroller. The real time values of different parameters were displayed at both transmitter and receivers side. Sensors used were tested for different environmental condition like changing the temperature across the fields. Also the wireless module was examined against different obstacles. It was found that system works well in transmitting real time values wirelessly to remotely placed receiver.

Fawakherji, M., Youssef, A., Bloisi, D., Pretto, A., & Nardi, D. [13]. Crop and Weeds Classification for Precision Agriculture Using Context-Independent Pixel-Wise Segmentation. Introduced deep learning based method to allow a robot to perform an accurate weed/crop classification using a sequence of two Convolutional Neural Networks (CNNs) applied to RGB images. The first network, based on an encoder-decoder segmentation architecture, performs a pixelwise, plant-type agnostic, segmentation between vegetation and soil that enables to extract a set of connected blobs representing plant instances. We show that such network can be trained also using external, ready to use pixel-wise labeled data sets coming from different contexts. Each plant is hence classified between crop and weeds by using the second network.

Todoroff, P., De Robillard, F., & Laurent, J.-B [14]. Interconnection of a crop growth model with remote sensing data to estimate the total available water capacity of soils introduces a simple and robust technique to estimate this parameter from optical satellite images and a dynamic semi-mechanistic crop growth model. The methodology is based on the inversion of sugarcane crop growth simulations made with the MOSICAS model and Normalized Difference Vegetation Index (NDVI) values extracted from SPOT images.

LIMITATIONS:

 From all the above mentioned papers we come across many limitations such as

·        The selection of seed was not done based on the environmental conditions

·        Sowing seed by maintaining certain distance between the seeds was not taken care as it varies from   seed to seed

·         For seed growth the seed has to be sown at a particular depth which was not carried out in the   projects before

·         If the project has to be made useful to the farmers then the robot has to automatic and the wheels of the robot should run smoothly on different environmental conditions.

All the above mentioned limitations are overcome by this project “Fuzzy Logic Based Automatic Seed Sowing Robot” where the seed selection follows a fuzzy logic approach