Soil Analysis and Crop Prediction

International Journal of Software & Hardware Research in Engineering, 2021

Agriculture is the primary source of livelihood which forms the backbone of many different countries. Current challenges in agriculture field due to traditional farming system necessitate farmers to be equipped with modern farming system. Machine learning technique helps to predict crop yield in agriculture that can help framers. In this system, we are using machine learning techniques which help to suggest the crops according to soil data. The model predicts and suggest most accurate crop based on the soil attributes. This prediction will help farmer to predict the yield of the crop before cultivating and thus help them to make appropriate decisions. Machine learning technique provides efficient framework for data driven decision making. This model provides set machine learning techniques to help farmers grow crops depending on their field important attributes.

European. Chemical. Bulletin. Special Issue, 2023

Agriculture sector works as the source of raw material for non-Agricultural sectors. As much as 60% of the land is used for farming in India. It feeds around total of 1.2 billion in population. The population has also been increasing day to day and also the agriculture is not able to meet the demanded requirements for the increasing population. Crop yields, meanwhile, have already begun to suffer as a result of climate change. Unnatural climate changes can have a negative impact on food production and forecasting, which in turn can affect farmers' economics by resulting in low yields. Droughts, floods, heat waves, storms, and various more extreme weather events can destroy crops and reduce harvests, and climate change can bring about these conditions. Crop output may also be impacted by variations in temperature and rainfall patterns, which may change when crops are planted and harvested. These changes can make it difficult for farmers to predict their yields accurately, leading to economic losses. It is possible to produce precise crop prediction results by using the right parameters, such as soil nutrients properties (Nitrogen, Phosphorous, and Potassium, nutrients concentration, soil type and pH value), rainfall patterns, temperature patterns, soil structures, and other factors, such as crop diseases. A crucial aspect of agriculture is determining the best crop to grow, and in recent years, machine learning algorithms have become increasingly important in this process. This unique research is use of ML algorithms to better precisely recommend the crops based on the location. Supervised learning classification was used for the recomme ndations in this study. This study's main goal is to identify the most effective feature selection and classification techniques to predict the best harvest that will thrive in a particular environment, including temperature, rainfall, and geographic location in a given state, soil properties, including phosphorus (P), potassium (K), nitrogen (N), and pH value, as well as soil type. To provide recommendations for crops that are likely to thrive in a particular environment based on the available soil nutrients, an agronomic advisor application can be developed. The application can use a suitable classification algorithm to identify the most relevant features of the soil and environment. This can help farmers optimize their crop production and maximize their yields.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Agriculture yield increase and agroindustry goods account for the majority of India's economy. They are the economic backbones of agricultural countries. Yield prediction is a crucial topic in agriculture. Any farmer wants to know what kind of harvest he may expect. Analyze the numerous connected parameters that are utilized to determine the alkalinity of the soil, such as location and pH value. Additionally, third-party apps such as APIs for weather and temperature, type of soil, nutrient value of the soil in that place, quantity of rainfall in that region, and soil composition are used to compute percentages of nutrients such as nitrogen (N), phosphorus (P), and potassium (K). To develop a model, each of these data properties will be evaluated and trained using various machine learning methods. The system includes a model that is precise and reliable in forecasting crop output and providing proper fertilizer ratio recommendations based on atmospheric and soil data. Farmers may also learn which crops are in great demand so that they can be readily produced. This application might be beneficial to all farmers who want to know which crops can be grown in various places or soils in order to optimize their profitability.

IRJET, 2020

2020

In India, the last drawback faced by the farmers is that the selection of correct yield for farming. Several factors influence the yield of crops like rain, temperature, soil, etc. Crop prediction helps farmers in choosing the right crop for plantation to maximize their earning. Prediction of crops could even be accurately through with the help of data mining techniques and considering the environmental parameters. During this work, the classifiers used area unit support vector machine and processing. Prediction of the crop is completed by considering parameters just like the quantity of rain, least and most temperature, soil type, humidity, and soil, pH, scale price. the knowledge is collected from the agricultural website of geographical area. the knowledge is split into 9 agricultural zones. The associate interface has been designed through that farmers will enter the specified information to predict the crop. Classification of soil is required so that farmers can recognize the s...

Indian Journal Of Agricultural Research, 2021

Background: The key source of income in India is agriculture, so farming is called as backbone of Indian economy. To satisfy the need of increasing population increase in the crop yield is very important. India country programming framework stated that, the annual soil loss in India is about 5.3 billion tonnes. Methods: Majority farmers are small or marginal scale and are dependent on natural resources like soil-quality, rainfall and environmental condition etc. for their yield. Based on experience farmers decide which crop to be adopted. Government is arranging trainings and exhibitions to enhance the skillset of farmers. Result: A land which gives poor yield for one crop may produce adequate yield for some other crop/crops. To know the possible suitable crop/crops proposed machine learning model focuses current and potential suitability evaluation for available scenario.

IJRASET, 2021

Agribusiness is the core of numerous nations and soil is the primary significant component of horticulture. There are diverse soil sorts and every sort has various highlights for various yields. In this field, presently a day's various techniques and models are utilized to build the amount of the harvests. So the primary motivation behind this of this task is to make a model that assists ranchers with realizing which harvest should take in a specific kind of soil. In this task, we measure the dirt pictures to produce an advanced soil characterization framework for rustic ranchers for minimal price. Tensorflow climate is utilized from this we can download the necessary bundles. We are utilizing two datasets, that is preparing set comprises of four sorts of soil Alluvial, Red, Dark, Earth and train set. Soil surface is the principle factor to be considered prior to doing development. In this methodology, we can gather 50 examples from the various areas of our country. The examples are shot under light condition utilizing an any camera. Soil pictures are handled through the various stages like Convolution layer is to separate highlights from the info picture, Max pool layer is to decrease the spatial component of the information volume for next layers, Drop out layer is arbitrarily sets input units to 0 with a recurrence of rate at each progression during preparing time, which forestalls over fitting, and different layers.

2021

Latest advances in integrated micro systems, incorporating the latest sensor technology, will enable affordable use of informatics in agriculture. This paper describes a method for a real-time prediction of soil properties using a portable, low-cost sensor incorporating an integrated circuit for soil impedance spectrometer. This new, application-specific integrated circuit was developed in our laboratory. The described method considers different soil sample properties variations of macro nutrients such as phosphorus, potassium and magnesium. The influence of machine learning parameters and soil categories was studied to obtain the most accurate model to meet the farmers' requirements for realtime soil analysis in an agricultural field. The system's portability allows the sensor to be attached to a tractor and quickly obtain a complete database of the field's soil properties and further fertilizer plan optimization.

Universal Journal of Agricultural Research, 2022

Agriculture is the backbone of India's economy, as it is the most important factor in the country's socio-economic development. Because of the rapid expansion in human population, the "Green Revolution" introduced high yield variety (HYV) seeds, which increased crop productivity but degraded crop and soil quality. This is due to the use of excessive amounts of chemical fertilizers in HYV seeds, as well as the irrigation system utilized to grow these seeds. This stunts the growth of the crops, resulting in financial and productivity losses. Because of field surveys, traditional ways to crop production prediction will take longer, and contemporary agriculture will face certain obstacles. As a result, a comprehensive review of various crop key factors such as climatic factors, soil nutrients, production factors, and environmental factors is conducted using a variety of machine learning approaches such as Support Vector Machine, bayes classifier, decision tree, random forest, linear regression and Extreme Learning Machines. The accuracy measures such as root mean square error, coefficient of determination and mean absolute error are used for comparing the performance of the system. Based on the findings of the reviews, an intelligent and robust machine learning technique provides the optimum option for achieving (i) soil fertility, (ii) crop prediction, and (iii) yield prediction. The importance of soil variables and the amount of nutrients available in the soil for growing crops has been found, according to an examination of 51 peer-reviewed studies, to create qualitative yield prediction. Furthermore, the investigations will yield recommendations for future fertilizer research.

IRJET, 2022

The use of the internet has brought vast numbers of users online through different platforms. Unlike the old days, the internet is not just limited to email surfing, there's so much on the internet or let's say everything is on the internet. The Internet has solutions for almost everything, from mental health to technical issues. In this agriculture, fields cannot be untouched. The 21 st Century has been the age of technology where technology has been used everywhere. So, for optimum results in each field, we use various methods which can minimize the loss and give us maximum benefits. The application of Machine learning in Crop type prediction for modern world farming is very much essential. Also, suggesting the type of fertilizer and amount can increase the usability of the application. Crop yield prediction and fertilizer suggesting application use machine learning algorithms to predict the crops yield based on various aspects: like the amount of rainfall and other different real-world parameters.