IoT sensors captures the soil characteristics and weather data, this data can be used to build a Deep Learning model which can recommend the most suitable crops to grow on a particular farm. This can help farmers to make decisions on which crops to grow on their farm.

The Crop Recommendation System targets to offer records to the farmers at the soil and crop traits. With this statistics the farmer can recognize which crop can be cultivated

This work presents a system, in a form of a website, which uses Machine Learning techniques in order to predict the most profitable crop in the current weather and soil conditions.

To address this challenge, a deep learning-based system has been developed to identify crop diseases and provide recommendations for suitable plants. The system utilizes RGB images to accurately identify diseases and suggest optimal crops.

As a result, the system will predict the above 80% best crop for given data of soil and nutrients, temperature and recommend fertilizers suitable for crop and soil using machine learning techniques from which farmers can grow their production.

Apr 15, 2023 · Therefore, in this paper, we propose a novel deep learning architecture for crop yield prediction, namely, SSTNN (Spatial-Spectral-Temporal Neural Network), which combines 3D convolutional...

The crop recommendation system project successfully leveraged data analytics and machine learning to provide tailored crop recommendations to farmers. It significantly improved crop yield and resource utilization, contributing to enhanced agricultural sustainability and profitability.

This method takes three parameters into consideration, viz: soil characteristics, soil types and crop yield data collection based on these parameters suggesting the farmer suitable crop to be cultivated.

Apr 24, 2025 · In this article, we'll create an image recognition model using TensorFlow and Keras. TensorFlow is a robust deep learning framework, and Keras is a high-level API(Application Programming Interface) that provides a modular, easy-to-use, and organized interface to solve real-life deep learning problem

This project aims Machine learning models can analyze soil data to provide customized recommendations for crop selection based on the specific characteristics of the soil. This can lead to more optimal and diversified cropping systems.