MACHINE LEARNING-BASED SYSTEM FOR CROP YIELD PREDICTION AND FERTILIZER RECOMMENDATION

Abstract

ABSTRACT A machine learning-based system (100) for crop yield prediction and fertilizer recommendation, comprising: a plurality of agricultural sensors configured to collect real-time data on soil moisture, temperature, and nutrient levels; a machine learning module integrated with the agricultural sensors, configured to analyze the collected data and identify agricultural patterns; a communication module within the system, configured to upload data to a control module; a processor within the control module, configured to manage and optimize the agricultural management process; a user interface configured to allow customization and real-time monitoring of agricultural parameters.

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to a field of agricultural technology and, in particular, to a machine learning-based system for crop yield prediction and fertilizer recommendation, enhancing precision, efficiency, and sustainability in agricultural practices.
BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Ensuring optimal crop yield and appropriate fertilizer application is a critical concern in agriculture, given the growing global population and the need for sustainable farm , Claims:WE CLAIM:
1. A machine learning-based system (100) for crop yield prediction and fertilizer recommendation, comprising:
a plurality of agricultural sensors configured to collect real-time data on soil moisture, temperature, and nutrient levels;
a machine learning module integrated with the agricultural sensors, configured to analyze the collected data and identify agricultural patterns;
a communication module within the system, configured to upload data to a control module;
a processor within the control module, configured to manage and optimize the agricultural management process;
a user interface configured to allow customization and real-time monitoring of agricultural parameters.

2. The system (100) as claimed in claim 1, wherein the agricultural sensors include soil moisture sensors, temperature sensors, and nutrient sensors.

3. The system (100) as claimed in claim 1, wherein the machine learning module is configured to provide real-time feedback and recommendations based on the analyzed data.

4.

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