Artificial Intelligence and Communication Technologies
Crop Recommendation System and Plant Disease Classification using Machine Learning for Precision Agriculture
Authors: Mahendra Choudhary, Rohit Sartandel, Anish Arun and Leena ladge
Publishing Date: 06-08-2022
ISBN: 978-81-955020-5-9
Abstract
The Agriculture sector is the backbone of our country. It provides a living for the vast majority of India’s inhabitants, but it only accounts for 15% of the country’s GDP. In comparison to other countries, our country’s crop yield is quite poor. This could be one of the reasons for India’s increased suicide rate among marginal farmers. Another cause for this is that farmers do not plan their crops properly. Another reason for this situation is that farmers frequently make incorrect crop selection decisions, such as planting in the wrong season or picking a crop that would not yield much for the particular soil. Incorrect crop selection will always result in a lower yield. It is difficult to survive if the family is entirely dependent on this revenue. In this paper, we offer a model that addresses these concerns. The suggested methodology allows for crop selection based on economic and environmental factors, intending to boost crop yields to satisfy the country’s growing food demand. The proposed model predicts the crop yield by studying factors such as rainfall, temperature, humidity, soil nutrients, ph value of the soil. The model assists farmers in maintaining soil nutrient levels. In addition to that, the app will enable farmers to identify diseases in their plants.
Keywords
Agriculture, Crop Suggestion System, Machine Learning, Fertilizer Recommendation, Plant Disease Classification.
Cite as
Mahendra Choudhary, Rohit Sartandel, Anish Arun and Leena ladge, "Crop Recommendation System and Plant Disease Classification using Machine Learning for Precision Agriculture", In: Saroj Hiranwal and Garima Mathur (eds), Artificial Intelligence and Communication Technologies, SCRS, India, 2022, pp. 39-49. https://doi.org/10.52458/978-81-955020-5-9-4