MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641085499 A) filed by Malla Reddy Engineering College For Women Autonomous; Malla Reddy University; Malla Reddy Mr Deemed To Be University; and Malla Reddy Vishwavidyapeeth Deemed on July 13, 2026, for Smart Crop Retailing: Integrating Customer Reviews With Crop Prediction.
Inventors include Dr. Y. Madhaveelatha; Dr. Malliga Thulasiraman; Mr. Perla Ramesh Babu; Mr. Vanga Rahul Reddy; Ms. Mahitha Dilli; Dr. A. Suresh Kumar; Mr. S Murali Krishnamachari; and Mrs. Tejavath Shashi Rekha.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: Agriculture plays a crucial role in the economic development of many countries, especially developing nations where a large portion of the population depends on farming as a primary source of livelihood. However, farmers frequently face challenges related to crop selection, market demand uncertainty, climate variability, and financial risks. Traditional farming decisions are often based on experience or local practices rather than scientific data analysis, which can result in low productivity, crop losses, and poor profitability. The present invention introduces a Smart Crop Retailing System that integrates crop prediction techniques with customer review analysis in order to provide intelligent crop recommendations to farmers and agricultural retailers. The system uses environmental parameters such as soil type, rainfall, humidity, temperature, and historical crop yield data to predict suitable crops using machine learning algorithms. At the same time, customer reviews and feedback collected from agricultural retail markets are analyzed using sentiment analysis techniques to determine market demand and consumer preferences. The system performs several stages including data collection, preprocessing, model training, prediction generation, and market sentiment analysis. Agricultural datasets are processed using data cleaning, normalization, and encoding techniques to prepare them for machine learning algorithms. Textual customer reviews are processed using natural language processing methods to classify customer opinions as positive, negative, or neutral. The predicted crop results are combined with customer sentiment insights to generate intelligent recommendations that are environmentally suitable and market-oriented. The system provides farmers with actionable insights that help them choose crops with higher demand and better profitability. The proposed invention helps reduce agricultural risks, improves crop planning efficiency, and enhances coordination between farming and retail markets. By integrating data analytics, machine learning, and customer feedback analysis, the system promotes smart agriculture and supports sustainable farming practices.
Disclaimer: Curated by HT Syndication.
