We built a Drone which can Estimate Yield and Disease in the Crop
We Team Agro-Vators under the guidance of our professor has done a project that can help farmers to solve their issues. This article will give you a brief idea about it.
In India, about 15,963 farmers are committing suicides every year. Among them, 20% is due to crop failure (mainly due to diseased crops) and about 35% is due to price crash and share bankruptcy (i.e., inaccurate Minimum Support Price (MSP) given by the government). As a consequence, it is leading to decrease in agriculture productivity, food scarcity, and an increase in consumer rates. Our project forms a solution to these issues.
TL;DR 😅, if you think that you can’t read a huge article ahead just watch this video 😉.
What is the actual issue?
In India, Mostly the yield of the crop is reduced due to diseases. If they have detected in the early stages itself, we can take necessary measures to get more yield. However, it is not an efficient process to diagnose each plant individually by the farmer and even by the remote sensing data (which is captured from satellite, because of its less resolution).
By taking previous year statistics and present year’s rainfall, amount of agricultural land, demand into account, the government decides MSP to the farmer. This MSP value depends on the supply. Both of them are inversely related. But, when the farmer gets less yield than expected from the land, he has to sell the yield at a higher price than MSP to avoid loss, But, As MSP is fixed by the government; either government or farmer has to suffer the loss.
Hence, we need to find a way to get stats of the current yield and also make sure we are able to diagnose the crop during the early stages of the disease. This project aims at achieving these goals.
How did we solve?
An area can be efficiently analyzed if it is done from the Ariel Perspective. Hence we decided to use a Drone for this project.
Step-1: The Drone used for this project has several advanced features such as Geo-Fencing, Path Tracing, Autonomous Flying, etc. The surveillance area of the field is initially fed to the drone. The drone captures all the required data by moving over the field in a particular predefined path.
Step-2: It is equipped with NIR and RGB cameras to capture high-resolution image data in Near IR and Visible Spectrums simultaneously. At the same time the gathered info from the drone is transmitted to the base station instantly (using Node MCU). At the base station, the received data is analyzed.
Step-3: We use NDVI (Normal Difference Vegetative Index) as the main parameter to measure the crop’s health status. Further feeding of these NDVI data to a Regression pattern generated by AI and ML algorithms(generated based on several test cases) gives precise information about the disease of the crop (cotton crop). Inturn it also helps us in the estimation of yield from the crop.
Now that we have gathered the required data. It helps the government in giving a better MSP (MINIMUM SUPPORT PRICE). Also, as we are having the health status of the crop we can help the farmer to determine the diseased zones in the cropland and thus helps in reducing the crop loss.
By 2025 it is estimated that India’s population increases by 10% (which demands an increase in Agricultural Productivity), It will be compulsory to gather more accurate crop data in a lesser period. Hence it is important to develop such mechanisms that can deliver more precise data in a smaller duration without the intervention of any human contact.
Testimonials
We also shared this idea with a few of the farmers of a village nearer to our college and they are quite excited to hear about this. Here is feedback from few of those farmers.
We have submitted this project for AICTE-Vishwakarma Awards-2019 under the theme ‘How to enhance the income of Villages through the intervention of Technology/Management practice(s).’ and got selected in the first phase. Thanks to our mentor who guided and helped us in every stage of our project.
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