Description:[0001] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0002] Precision agriculture is a farming management concept based on observing, measuring and responding to inter and intra-field variability in crops. The goal of precision agriculture research is to define a decision support system (DSS) for whole farm management with the goal of optimizing returns on inputs while preserving resources. Farmers can control all the processes remotely with a precision agriculture system.
[0003] A number of different types of crop yield prediction systems that are known in the prior art. For example, the following patents are provided for their supportive teachings and are all incorporated by reference.
[0004] Machine Learning- and Feature Selection-Enabled Framework for Accurate Crop Yield Prediction:- Agriculture is crucial for the existence of humankind. Agriculture provides a significant portion of the income for many people all around the world. Additionally, it provides a large number of work possibilities for the general public. Numerous farmers desire for a return to the old-fashioned techniques of farming, which provides little profit in today’s market. Long-term economic growth and prosperity are dependent on the success of agriculture and associated companies in the United States. Agribusiness crop yields may be increased by carefully selecting the right crops and putting in place supportive infrastructure. Weather, soil fertility, water availability, water quality, crop pricing, and other factors are taken into consideration while making agricultural predictions. Machine learning is critical in crop production prediction because it can anticipate crop output based on factors such as location, meteorological conditions, and season. It is advantageous for policymakers and farmers alike to be able to precisely estimate crop yields throughout the growing season since it allows them to anticipate market prices, plan import and export operations, and limit the social cost of crop losses. The use of this tool assists farmers in making informed decisions about which crops to grow on their land. In this study, a machine learning framework for agricultural yield prediction is presented. Crop information is collected in an experiment’s data set. Then, feature selection is performed using the Relief algorithm. Features are extracted using the linear discriminant analysis algorithm. Machine learning predictors, namely, particle swarm optimization-support vector machine (PSO-SVM), K-nearest neighbour, and random forest, are used for classification.
[0005] Detection and Classification of Plant Leaf Diseases by using Deep Learning Algorithm:- Plant leaf diseases and destructive insects are a major challenge in the agriculture sector. Faster and an accurate prediction of leaf diseases in crops could help to develop an early treatment technique while considerably reducing economic losses. Modern advanced developments in Deep Learning have allowed researchers to extremely improve the performance and accuracy of object detection and recognition systems. In this paper, we proposed a deep-learning-based approach to detect leaf diseases in many different plants using images of plant leaves. Our goal is to find and develop the more suitable deep- learning methodologies for our task. Therefore, we consider three main families of detectors: Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN), and Single Shot Multibook Detector (SSD), which was used for the purpose of this work. The proposed system can effectively identify different types of diseases with the ability to deal with complex scenarios from a plants area.
[0006] Precision farming aims to improve crop performance and environmental quality. It is defined as the application of technologies and principles to manage spatial and temporal variability associated with all aspects of agricultural aspects. The proposed invention focuses on predicting the crop yield using feature-based selection algorithm.
[0007] Above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, no assertion is made, and as to whether any of the above might be applicable as prior art with regard to the present invention.
[0008] In the view of the foregoing disadvantages inherent in the known types of crop yield prediction systems now present in the prior art, the present invention provides an improved system. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved system based on feature selection-based algorithms for accurate prediction in agriculture that has all the advantages of the prior art and none of the disadvantages.
SUMMARY OF INVENTION
[0009] In the view of the foregoing disadvantages inherent in the known types of crop yield prediction systems now present in the prior art, the present invention provides an improved one. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved system to predict the crop yield and growth using feature selection-based algorithms which has all the advantages of the prior art and none of the disadvantages.
[0010] The main objective of the proposed invention is to design & implement a framework of feature selection-based algorithms for crop yield prediction. The invention focuses on predicting the crop growth and achieves precision in agriculture.
[0011] Yet another important aspect of the proposed invention is to monitor the crops for their growth and yield. The invention includes a camera for monitoring the crops for their features based on feature selection-based algorithm. The invention aims at improving the crop yield. The ultimate goal is to achieve precision in agriculture.
[0012] In this respect, before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the various ways. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[0013] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be had to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BREIF DESCRIPTION OF DRAWINGS
[0014] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
Figure 1 illustrates the schematic view of implementation of feature selection-based algorithms for accurate prediction of crop yield and paving way for precision in agriculture, according to the embodiment herein.
DETAILED DESCRIPTION OF INVENTION
[0015] In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that the embodiments may be combined, or that other embodiments may be utilized and that structural and logical changes may be made without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents.
[0016] While the present invention is described herein by way of example using several embodiments and illustrative drawings, those skilled in the art will recognize that the invention is neither intended to be limited to the embodiments of drawing or drawings described, nor intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention covers all modification/s, equivalents and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. The headings are used for organizational purposes only and are not meant to limit the scope of the description or the claims. As used throughout this description, the word "may" be used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Further, the words "a" or "a" mean "at least one” and the word “plurality” means one or more, unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and any additional subject matter not recited, and is not intended to exclude any other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like are included in the specification solely for the purpose of providing a context for the present invention.
[0017] In this disclosure, whenever an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same element or group of elements with transitional phrases "consisting essentially of, "consisting", "selected from the group consisting of”, "including", or "is" preceding the recitation of the element or group of elements and vice versa.
[0018] Machine learning (ML) algorithms have emerged as promising alternative and complimentary tools to the commonly used modelling approaches in agriculture and allied sciences. Feature Based recognition computes the responses of a set of feature detectors over an image and collects them into a feature vector. Feature selection can impact a machine learning model’s performance by defining a significant feature subset for increasing the performance and identifying the variability.
[0019] Precision agriculture benefits to the environment come from more targeted use of inputs that reduce losses from excess applications and from reduction of losses due to nutrient imbalances, weed escapes, insect damage etc. other benefits include as reduction in pesticide resistance development. The proposed invention focuses on predicting the crop yield using feature selection-based algorithm.
[0020] Reference will now be made in detail to the exemplary embodiment of the present disclosure. Before describing the detailed embodiments that are in accordance with the present disclosure, it should be observed that the embodiment resides primarily in combinations arrangement of the system according to an embodiment herein and as exemplified in FIG. 1
[0021] Figure 1 illustrates the schematic view of implementation of feature selection-based algorithms for accurate prediction of crop yield and paving way for precision in agriculture 100. The proposed system 100 includes a camera 101 for monitoring the crops 102 which are analyzed for their growth and crop yield. The data from camera 101 is sent to the feature selection-based algorithms 103. The results of feature selection unit 103 will display the results of feature selection on display unit 104. The prediction of crop yield and crop growth will take place over the cloud 105.
[0022] In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide a thorough understanding of the arrangement of the system according to an embodiment herein. It will be apparent, however, to one skilled in the art that the present embodiment can be practiced without these specific details. In other instances, structures are shown in block diagram form only in order to avoid obscuring the present invention.
, Claims:1. Implementation of Feature Selection-Based Algorithms for Accurate Prediction of Crop Yield and paving way for Precision in Agriculture comprises of
Machine learning unit;
Cloud server;
Camera;
Selection unit and
Display unit.
2. Implementation of Feature Selection-Based Algorithms for Accurate Prediction of Crop Yield and paving way for Precision in Agriculture, according to claim 1, includes a machine learning unit, wherein the machine learning unit will analyse the data recorded regarding the crop growth and crop yield.
3. Implementation of Feature Selection-Based Algorithms for Accurate Prediction of Crop Yield and paving way for Precision in Agriculture, according to claim 1, includes a cloud server, wherein the cloud server will establish communication and coordination among various components.
4. Implementation of Feature Selection-Based Algorithms for Accurate Prediction of Crop Yield and paving way for Precision in Agriculture, according to claim 1, includes a camera, wherein the camera will capture and record the information regarding crops.
5. Implementation of Feature Selection-Based Algorithms for Accurate Prediction of Crop Yield and paving way for Precision in Agriculture, according to claim 1, includes a selection unit, wherein the selection unit will classify the crops according to the features.
6. Implementation of Feature Selection-Based Algorithms for Accurate Prediction of Crop Yield and paving way for Precision in Agriculture, according to claim 1, includes a display unit, wherein the display unit will display the results of feature selection algorithm.