Pest Control and Agriculture using YOLO and CNN

The ideology of this project presents the detection and Classification of pests using YOLO AND CNN. The brisk increase of human population leads to the increase in the demand of the food. Due to the illiteracy rate and deprivation in our country we mislay a large amount of crops due to climatic cond

Project Title

Pest Control and Agriculture using YOLO and CNN

Project Area of Specialization

Artificial Intelligence

Project Summary

The ideology of this project presents the detection and Classification of pests using YOLO AND CNN. The brisk increase of human population leads to the increase in the demand of the food. Due to the illiteracy rate and deprivation in our country we mislay a large amount of crops due to climatic conditions and pests. Enormous quantities of crops are ruined annually due to the presence of pests. So the pest must be detected and classified in order to guarantee superior production in agricultural fields. Prior detection of pests in the image is important for control of pests in the fields. Due to this, classifying the pest in the images has been an onerous task. The main intention of this project is to classify the pests and to apply certain measures to protect the crops from the pests. For detection of pest we use YOLO (You look only once) algorithm and for classification of pest we use CNN (Convolution Neural Network).

Project Objectives

The output of the pest detection and classification involves the detection of pest by the bounding box around the pest and identification of pest by give label to the pest. The Graphic User interface is used for the representation of output of the pest detection and classification. Initially we will browse the image from the database and the detection button on the GUI is pressed after that both pest detection and classification output is displayed. The pest detection and classification can be observed on single pest in an image or multiple pests in an image. The following figures show the output for single and multiple pests.

Project Implementation Method

1. Database

The pest images are being taken manually from the internet. The size of the database can be varied. In this method we use four different types of pests namely Colorado beetle, Grasshopper, Japanese beetle, Lady Bug. These pests are present on leaves and flowers. The database consists of pest images which are being captured in various angles. For non-real time purposes the database is prepared by manually collecting the images where the pests are present. 

Pre-processing

The pre-processing of the image from the database involves the resizing of the image as per size required by the networks used in the methodology. There are 2 networks used and the input image is being resized without changing the aspect ratio of original image. The aspect ratio states as the ratio of height and width of an image. The resizing operation is the only operation performed in pre-processing. 

YOLO Based Segmentation

YOLO (You Look Only Once) is a real time object detection system. There are different object detection techniques present which include Regional Convolution Neural Network and You look only once (YOLO) v2, v3. For this pest detection we use yolov2ObjectDetector. YOLO v3 object detector upgrades upon YOLO version2 object detector by adding detection at different scales to assist detection of min objects. The YOLO v2 is sufficient for pest detection on the crops. 

Pest detection

The output of YOLO based segmentation gives the detection of pest in an image. The detected pest on crop is not efficient for elimination of them; the pests must be classified to identify the type of pest present on the crop. The ROI of the image is given to the CNN which classifies pest present.

Alexnet CNN

Alexnet is used for image classification. Alexnet is used to identify the pest and YOLO is used for pest detection. Alexnet is used in labeling of the pests present in an input image. Alexnet is a CNN so we need to train the network same as the YOLO network. The first step involves the loading a pre-trained neural network. The layer architecture is reviewed and the next step involves the modifying the pre- trained network.

Pest Classification

The final step after training involves the identification of pest present on the image. The pest classification gives the name of the pest present in the input image. The single pest or multiple pests are being detected and classified.

Benefits of the Project

The pest detection and classification is performed for detection of pests at early stages such that the farmers use proper amount of pesticides to kill the pests. The pests are classified so to use the required pesticide on the particular pest. The pest detection and classification can be implemented in hardware and the farmers could use at a wide range so they could save the fields from the pests. The future scope in the pest detection involves the usage of the pest detection and classification method by the farmers in a wide range as of present our country is being developed by the latest advancement in the technologies we need some more time for usage of this application in a wide range.

Technical Details of Final Deliverable

The Crop Protectors can anticipate some pest threats and take action to prevent pest populations from growing to a destructive level.

Crops can withstand a little damage from insects, diseases and weeds, but if the pest takes over the farm field, it can have a big impact on the farmer’s livelihood and the region’s food supply. Farmers use a wide variety of agronomic practices to prevent pest populations from building up to economically damaging levels.

Here are some of the Prevention tools in a farmer’s Integrated Pest Management (IPM) toolbox:

CROP LOCATION

Growing crops in locations where they are best suited to the climate, soil and topography provides them with optimal conditions from the start.

VARIETY SELECTION

Choosing beneficial crop varieties, such as those with disease and pest resistance, has always been a cornerstone of IPM. This might include choosing conventional or biotech varieties.

STRATEGIC PLANTING AND CROP ROTATION

Sowing different crops in alternate rows or under sowing a crop like maize with a legume can improve soil fertility and reduce weeds. Growing different crops in rotation also helps reduce the build-up of pests.

SOIL MANAGEMENT

Mechanical, physical and cultural crop protection methods prevent or minimize pests. These methods also reduce their build-up and carryover from one crop to another.

 WATER MANAGEMENT

Careful irrigation can control weeds, save water and protect beneficial soil organisms.

OPTIMIZING PLANT NUTRITION

Applying nutrients at the right time in the correct amounts can optimize soil health and help crops withstand attacks from pests.

HARVESTING AND STORAGE

Good harvesting, seed cleaning and storage methods can reduce the carryover of weed seeds and disease-causing organisms.

PRESERVING BIODIVERSITY

Protecting natural habitats near farmland is the best way to conserve biodiversity, including many natural pest enemies.

Final Deliverable of the Project

Software System

Core Industry

Agriculture

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Zero Hunger

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com