MACHINE LEARNING BASED SYSTEM FOR OPTIMIZED FIELD-SPECIFIC AGRICULTURAL MANAGEMENT

Abstract

Machine learning (ML) is an emerging technology with diverse applications, including image recognition and healthcare, and it is increasingly relevant in agriculture. Crop yields are influenced by various factors such as soil type, temperature, moisture, and rainfall, yet crops are often unevenly distributed across fields, limiting resource utilization. Although technologies like artificial intelligence (AI), ML, and the Internet of Things (loT) have the potential to address agricultural challenges, they remain underutilized by farmers. This research focuses on predicting crop yields and mapping yield distribution using the Random Forest (RF) algorithm, a widely used supervised learning technique for regression and classification. By analyzing data on factors affecting agriculture, the project aims to create an interactive, user~friendly interface that provides farmers with yield maps and actionable recommendations for optimizing low-yielding areas, ultimately enhancing agricultural productivity and resource efficiency.

Specification

The field of invention of the project pertains to boosting low productivity regions by promoting
more efficient precision agriculture and agricultural technology. It utilizes machine learning
(ML), specifically the Random Forest algorithm, and various sensors (such as temperature,
humidity, and NPK sensors) to predict crop yield and make crop recommendations based on
environmental conditions like soil type, temperature, and moisture. The goal of this invention is
to enhance the efficient use of farmland by providing farmers with accurate yield predictions and
suggestions for improving low-yield areas, encouraging more effective and sustainable
agricultural methods.
BACKGROUN:D OF INVENTION
AUTHOR NAME : David Patrick Perry et al
PATENT NO : US 11263 707B2
DESCRIPTION:
A crop prediction system utilizes a range of machine learning techniques to forecast agricultural
yields and to determine a series of farming practices that, when implemented, enhance crop
production. This system employs crop prediction models that have been developed through
various machine learning processes, taking into account geographic and agronomic data. Upon
receiving a request from a farmer, the crop prediction system can retrieve information pertaining
to a specific parcel of land, including its location, prevailing weather conditions, and soil
characteristics. The system then applies one or more crop prediction models to this information
to estimate crop yields and to recommend an optimized set of farming practices for the farmer to
adopt.
DESCRIPTION:
Crop yield can be evaluated and forecasted through a piecewise linear regression approach that
incorporates break points along with various meteorological and agricultural factors, including
NDVI, surface characteristics (such as soil moisture and surface temperature), and precipitation
data. These factors are instrumental in estimating and forecasting crop conditions. The overall
environment for crop production may exhibit inherent heterogeneity and nonlinear
characteristics. To formulate an empirical equation for crop yield prediction, a non-linear
multivariate optimization technique can be employed. The Quasi-Newton method may facilitate
optimization by reducing discrepancies and errors in yield forecasts. The minimization of the
least squares loss function can be achieved through the iterative convergence of a pre-defined
empirical equation, utilizing the piecewise linear regression method with break points. This nonlinear
approach can yield acceptable lower residual values, with predicted outcomes closely
aligning with observed data. The current methodology can be adapted and customized for
various crops across the globe.
AUTHOR NAME : Robert James Lindores
PATENT NO:US20140012732Al
DESCRIPTION:
In a process for producing crop recommendations, a computer system acquires multiple data sets
from various distinct data sources, with each data set detailing a factor that influences crop
performance. The computer system establishes a benchmark for each data set, illustrating the
impact of the factor on the market value of the crop. Subsequently, -the computer system
formulates a model that characterizes the crop based on the benchmarks derived from the
multiple data sets. Finally, the computer system generates a report that includes at least one
suggestion aimed at enhancing the market value of the crop.
The project initiative utilizes machine learning, particularly the Random Forest algorithm, to
forecast crop yields and recommend suitable crops based on environmental data such as
temperature, humidity, soil nutrients (NPK), and pH levels. By collecting and analyzing these
data through multiple sensors, the system assists farmers in optimizing their fields by predicting
the yield potential and offering practical actionable recommendations to improve low-yield
areas. The project also includes a user-friendly interface that allows farmers to interact with the
system,. helping them make informed decisions about crop selection and field management,
ultimately improving agricultural productivity and sustainability.The project promotes efficient
resource use and sustainable farming practices,addressing the growing global food demand while
fostering environmental management.
OBJECTIVES
I. Crop Yield Prediction: Utilize machine learning techniques to accurately forecast crop
yields.
2. Proposing Yield Improvement Solutions: Provide data-driven recommendations to
enhance crop yield in underperforming regions.
3. Optimizing Farming Practices: Offer actionable insights to improve the efficiency of
agricultural. operations.
4. Sustainable Agriculture: Promote sustainable farming by optimizing the use of
resources and minimizing waste.
5. User-Friendly Interface for Farmers: Design an intuitive, easy-to-use interface for
farmers to access data and recommendations efficiently.
Agricultural landscapes often display fluctuations in crop yields, influenced by variations in soil
characteristics, moisture levels, nutrient accessibility, and various environmental factors.
Contemporary farming methods frequently do not fully leverage the latest technological
innovations, resulting in suboptimal crop management and· resource distribution. This system
aims to address these shortcomings by providing accurate, data-informed insights regarding field
conditions and yield potential. It enables farmers to make well-informed choices, enhancing the
utilization of resources like water, fertilizers, and land to boost productivity and enhance crop
yields.
Agricultural landscapes often display fluctuations in crop yields, influenced by variations in soil
characteristics, moisture levels, nutrient accessibility, and various environmental factors.
Contemporary farming methods frequently do not fully leverage the latest technological
innovations, resulting in suboptimal crop management and resource distribution. This system
aims to address these shortcomings by providing accurate, data-informed insights regarding field
conditions and yield potential. It enables farmers to make well-informed choices, enhancing the
Q) utilization of resources like water, fertilizers, and land to boost productivity and enhance crop
The system utilizes Internet of Things (loT) devices to deliver precise and real-time insights by
gathering data from the field. These devices assess critical environmental factors, which include:
1. Temperature and humidity sensors that observe climatic conditions.
2. NPK sensors that evaluate the levels of vital nutrients in the soil, specifically nitrogen,
phosphorus, and potassium.
3. Soil pH sensors that ascertain the acidity or alkalinity of the soil.
4. 4.Soil moisture sensors that monitor the water content present in the soil.
Instead of transmitting sensor data directly to the web application in real-time, the system opts to
store the measurements in a CSV file. This file encompasses numerous data points for each
sensor attribute, reflecting values gathered over a specified duration. This method guarantees that the system can function effectively in settings where continuous real-time data collection may
not be feasible, such as locations with restricted connectivity or sporadic sensor availability.
The system analyzes the stored CSV file to calculate the average values for each sensor attribute,
such as temperature, humidity, NPK concentrations, and soil pH. These averaged values yield a
more consistent and representative dataset for subsequent analysis, mitigating fluctuations that
may arise from environmental factors or sensor irregularities. After computing these averages,
they serve.as input for the machine learning model.
The Random Forest algorithm utilizes these averages to:
I. Forecast crop yields based on the existing conditions of the field.
2. Advise on the most suitable crops to cultivate, taking into account the environmental data
and soil · composition.
The system has been developed with a strong emphasis on user-friendliness, allowing farmers to
effortlessly access and understand the data. The index.htrnl page of the website presents the
averaged sensor values directly to the user, offering a comprehensive overview of the current
conditions in the field.
Upon clicking the Get Recommendation button, the system activates the machine learning model
to analyze the data and determine the most appropriate crop for the prevailing conditions.
Subsequently, the user is redirected to the result.htrnl page, where the suggested crop is
showcased. This interface is designed to ensure that even those farmers with minimal technical
expertise can utilize the system effectively to improve their agricultural decisions.
Claim I : A site-specific crop management system that predicts yields and recommends suitable
crops using a Random Forest machine learning model trained on sensor data from environmental
conditions.
Claim 2 : The system of claim I processes environmental data from a CSV ·file to compute
average sensor values for crop yield prediction and recommendations.
Claim 3: A web interface for the system of claim I displays averaged sensor values on an
index.html page, allowing users to trigger crop recommendations via a Get Recommendation
button that redirects to a result.html page.
Claim 4 : The system of claim I uses the Random Forest algorithm for crop recommendations
based on environmental sensor data.
Claim 5 : The system of claim I includes a user interface for farmers to input specific field
conditions and receive customized crop management advice, including yield predictions.
Claim 6 :The system of claim I includes sensors that measure nitrogen, phosphorus, potassium
levels, and soil pH, which are essential for accurate crop recommendations.
Claim 7 : A method to improve agricultural productivity that involves collecting loT sensor data
(temperature, humidity, NPK, pH), storing it in a CSV file, computing average values, and using
a Random Forest model for crop prediction and recommendations.
Claim 8 : The system of claim I enables data-driven decisions by analyzing environmental
sensor data, optimizing crop selection based on soil composition, temperature, and humidity.
Claim 9 : The system of claim I can operate without continuous real-time data integration, using
historical data stored in a CSV file for analysis and recommendations.
Claim 10: A crop management system as described in claim I, built on the Flask framework,
providing an intuitive graphical user interface for farmers to obtain crop recommendations

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