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Overview
Why focus on productivity and improved production?
The world's population is rapidly growing, increasing the demand for food and employment. The global population is set to hit 9.8 billion by 2050, demanding around 14,886 million tons of World cereal equivalent (CE) food. Astoundingly, upto 40 percent of global crop production is annually lost to pest-related issues. This highlights the urgency of rethinking agricultural practices to improve yields and meet the rising food requirements.
As a result, new automated methods are being introduced to meet food requirements because traditional methods used by farmers are insufficient to meet these requirements. Efforts and research are underway to improve the quality and quantity of agricultural products by making them “connected” and “intelligent” through “smart farming.
The Agriculture industry is revolutionalizing this shift towards efficiency and improved productivity by adopting Artificial Intelligence (AI) based tools and systems, replacing conventional methods with advanced, effective approaches. Technologies such as Internet of Things (IoT), Big Data, Artificial Intelligence (AI), and Machine Learning (ML) have made inroads into our agriculture industry.
Whether it is weed control, calculating the best time to harvest crops, monitoring soil and crop health, precision agriculture management or predicting yield in advance, AI is already being used as part of Smart farming.
According to MarketsandMarkets, the market for artificial intelligence (AI) in agriculture is anticipated to expand from USD 1.7 billion in 2023 to USD 4.7 billion by 2028.
To be interested in food but not in food production is absurd. - Wendel Berry
Objective of the Project:
What and how of proposed AgriiVeda Web and Mobile Application..
Problem Statement
Farmers are regarded to be Bharat's backbone, thus there is a need to support them technologically in order to overcome the obstacles of conventional agricultural techniques while also having a positive impact on yield, harvest, healthy crop output, and ultimately farmer's revenue. To raise healthy crops, farmers must be aware of their soil and learn how to enhance it. Many farmers still need to be educated about soil to encourage healthy crop growth, enhance yield, and their revenue.
Soil is the vital component of agriculture. Crop production can be maximised when the soil's biological, chemical, and physical conditions are optimal.
In order to choose the best crops for their land, farmers would benefit from information about the soil, such as fertility level, missing macro/micro nutrients & components in the soil, and crops that are best suited to cultivate for their soil type. If farmer grows the crops by considering soil parameters, it would turn into a boon for him in future as he can expect healthy crop growth, more yield and better income.
Solution
AgriiVeda proposes an approach which involves artificial intelligence (AI) and machine learning (ML) features to help our farmers with Soil Fertility Rate (SFR) & Crop Prediction.
We propose to develop a deep neural network application using AI/ML algo to predict the best crop which can be suitably grown in the soil. Soil parameters such as nitrogen, phosphorous, potassium (NPK), pH, organic carbon, moisture content, electric conductivity, organic carbon, and few more things are considered for predicting the fertility of the soil and also to predict the right crops to be grown and nutrition required for it.
Our artificial Intelligence model will utilise the data of soil parameters to predict soil fertility rate, recommend crops and nutrition in minimum time and cost.
If the farmer knows about the crops which can be grown in his land, he can input the same to the system or else we will use soil sensors that can capture the soil parameters like NPK, pH level, moisture, temperature, electric conductivity, organic carbon level and so on. Similarly, the farmer/grower can input the historic soil card data or can get soil card made from the district level soil testing labs for soil fertility testing and crop suggestion.
The Crop Prediction System (CPS), coupled with the crop data set, which includes soil parameters and crop growth data, would predict crops (3 to 4) that could be cultivated with the provided soil sample. The Nutrition Suggestion System (NSS) would take the crop recommender system's output and, in conjunction with the Fertiliser data, propose the necessary nutrients or fertilisers to increase fertility and help grow each crop suggested.
The soil fertility rate, crop recommendations, and nutrition or fertiliser suggestions would be saved in the cloud and provided to farmers via phone in the form of Soil Reports (SR) that can be easily understood.
We will
We will further implement Farm Activities Management (FAM) feature to smartly manage the farm on a day to day basis.
Information such as Soil card data, historical weather’s data, water availability, and availability of various nutrients in the soil will help farmers grow the most profitable crop per acre.
Key Features
- With the help of AI the app will offer knowledge/information on the soil health management, crop-specific nutrient needs, and sustainable farming methods. This will help improve the crop production and overall revenue of the farmers over time.
- Thru the application we will make it easier for a farmer to book a soil sample collection request, get them tested, and receive recommendations online. This will save time, reduce paperwork, and help manage data better.
- Optimize the efficiency of fertilizers and crop protection inputs to enhance crop quality and yield
- With the help of our app farmers can safeguard soil health while minimizing the environmental impact from using excessive fertilisers damaging the crop and the nutritional values of the soil.
- Enhance farming practices and bring down costs.
Information such as Soil card data, historical weather’s data, water availability, and availability of various nutrients in the soil will help farmers grow the most profitable crop per acre.