Forecasting the Yield using Biomass Calculated from Satellite Images

Project Title

Forecasting the Yield using Biomass Calculated from Satellite Images

Project Area of Specialization Artificial IntelligenceProject Summary

Population growth has typically increased the need for beforehand planning of crop production and forecasting the yields of crops using biomass. Crop yield is mandatory, predominantly in those countries where agriculture is their main source of economy. These predictions help in estimating the reduction of crop yields so that an effective import and export system is produced. The procedure to estimate the yield before the crops are harvested using satellite remote sensing techniques is very important these days. Our project is mainly focused on how much yield a crop will produce using biomass with the help of remote sensing techniques through satellite images. For this purpose, we evaluated different techniques and scenarios, calculated different time series data for the purpose of finding vegetation indices which will further be used in estimating correct yield. Finding vegetation indices and their approximate values to predict the future crop yield is one of the best methods to assess plants future yield. We used sentinel-2 satellite for our data collection of Vegetation Indices. Vegetation Index in remote sensing simple explains about the vegetation biomass for every pixel in the remote sensing technique. The indices are obtained with the help of various spectral bands reflectance. In this regard, we calculated Normalized Difference Vegetation Index (NDVI), which tells about the greenness in a plant and helps in estimating the yield. Moreover, we also calculated Leaf Area Index (LAI) with the help of NDVI. Leaf Area Index explains about the leaf area per unit ground area and it is obtained using SNAP toolbox which is the efficient software for processing satellite data. After the implementation of the required methodology, results will be achieved and according to that analysis will be conducted to get the required estimated yield.

Project Objectives

Yield forecasting plays a significant role in different levels of classes including stakeholders, farmers, decision makers and policy makers. Yield forecasting has been a puzzle for different researchers and developers due to the lack of real time data or different environmental factors [1]. Our project aims at developing ease in forecasting the yield using biomass and has the following objectives listed below:

• Review of the present seasonal crops’ yield for assisting the farmers when to harvest the crop in its full productivity.
• Remote sensing imagery to be applied on different regression models to timely predict the yield of crop.
• Lessen the overall cost for the agriculture community to imply the stature of crop when to be cultivated.

Limit the crop loss due to various calamities and recognize the specific harvest planted area using remote sensing.

Project Implementation Method

A linear regression model has to be used because it will evaluate the perfect linearity between different variables and for the analysis between the predicted and actual value, root mean square error is the technique that will tell if the value is greater than some threshold the relationship is high otherwise it is not. Thus, the main task relies on the data set which is significantly the serious part of any model calibration. The method is actually about how to obtain the at all sensed data and then how to smear regression analysis on that data to get the accurate yield results. The rice and cotton yield data has been deliberated for this report and it is collected from the official Sentinel Hub website which ensures real-time data collection. For that, there are different ways on how to acquire data based on agricultural indices which are various including the atmospheric and environmental factors and these factors give a clear indication about the environment being faced at the crop site. The area which we are targeting is Rahim Yar Khan and the geographical coordinates are obtained from the GeoJSON. Once the Area of Interest (AOI) is marked the data is collected using the time filter available on the website and the real-time data is collected which can be visualized too for ease. Moreover, it is downloaded which requires heavy space on the disk. Vegetation Index (NDVI) is a useful tool in the calibration of Leaf Area Index (LAI). These vegetation indices give special reflectance in different bands which are collected through real-time data and useful information is extracted using satellite imagery processing toolboxes like SNAP and ArcMap. In our research work, the area of Rahim Yar Khan is focused and the data has been collected starting from the Kharif season (April 2020-June 2020) till the end of it. The data acquired is in the “tiff” format which gives the idea about additional information associated with the data like map projections. The exact values of NDVI are extracted from SNAP toolbox which gives the ranges of NDVI values within the real time data and uses different bands to calculate the value of NDVI and as the exact values are determined it will be successfully used in calculating the LAI value and it is the best estimator of plant’s biomass which will be further used in yield regression analysis. For the collection of data, different geographical points have to be used in order to get exact crop data. There are many formats which can be used to get these values like getting the NDVI in pure image format where different levels of colors can then distinguish which NDVI values are best to be used. Therefore, we have used the exact NDVI values for our project and this data is of high importance and will further be used for the real time crop data. The advanced search facility in EO browser helped us to get the 0% cloud coverage data.

Benefits of the Project

While crop yield forecasting is important for national food security. including early determination of the import/export plan and price, it is also important in providing timely information for optimum management of growing crops.

Lack of reliable and up-to-date information on supply, demand, stocks, and export availability

Weaknesses at the national level to produce consistent, accurate, and timely agricultural market data and forecasts

Inadequate information on stocks, domestic prices, and linkages between international and domestic markets

Inappropriate and/or uncoordinated policy responses to market crisis

Technical Details of Final Deliverable

Our project facilitates the administration and decision-makers to formulate the policies and budget by providing them with the application, which can forecast the crop yield by real-time data using remote sensing imagery. Recent advancement in technology has paved the way for developing smart agriculture in the outside world. Before the harvest of crops, yield can be computed between different intervals of time on the ground rules of Artificial Intelligence. Satellite remote sensing devices offer an exclusive outlook on the condition and active changes occurring inland, coastline, and oceanic ecosystems. This application will make use of remote sensing imagery, which will predict the crop yield using different regression models, which will keep track of the increment in plant biomass from the initial stage of growth to its maturity stage. The final deliverable will include a full fledge model that will help in forecasting the yield through a regression model. The equation's accuracy hence will tell the accuracy of yield.

Final Deliverable of the Project Software SystemCore Industry AgricultureOther Industries Food Core Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Zero Hunger, Good Health and Well-Being for People, Decent Work and Economic GrowthRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	10000
Dataset	Miscellaneous	1	10000	10000