A Smart Agricultural Assistant for Crop Recommendation using Machine Learning

This paper presents a Smart Agriculture Assistant that uses data analytics and machine learning to assist farmers in making informed decisions about various farming practices. The assistant offers a holistic approach to enhance agricultural decision-making process by recommending crops based on soil attributes, climate, and local weather conditions, as well as providing precise fertilizer and pesticide guidance tailored to Indian contexts. Data is collected from various sources, such as weather forecasts, groundwater data, soil maps, and crop prices. Machine learning algorithms are trained on that data to generate optimal recommendations for farmers. The study demonstrates the effectiveness of smart agricultural techniques over conventional methods in improving crop selection accuracy and optimizing fertilizer application, thereby enhancing agricultural productivity. Furthermore, the integration of image analysis technology using computer vision enables the system to assess crop quality parameters, aiding in the detection of grade, size, colour, and disease presence. It also provides insights of estimated realtime crop prices by taking into account the fickle market dynamics. This offers valuable information to the farmers for increasing profit margins. The assistant is implemented as a web application that can be accessed by farmers from anywhere. This research provides a framework for the integration of data-driven technology to solve the unique issues encountered by Indian farmers. The Smart Agriculture Assistant presented here represents the power of data science and deep learning for a more prosperous and sustainable future for Indian agriculture.