Crop Prediction and Plant Disease Detection Using Machine Learning
Ms. Anamika Wasnik1, Mrs. Sinu Nambiar2 ,Rajvi Deshmukh3, Anuja Marwadkar4, Nidhi Meena5
1,2,3,4,5 Dept. of Computer Science Engineering
1,2,3,4,5 Dr. D.Y. Patil Institute of Technology, Pune, Maharashtra, India
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Abstract - In general, India's economy relies heavily on agriculture, which also contributes a significant amount of the country's gross domestic product to the nation's efforts to secure food security. Yet, due to manmade climatic changes, food production and forecasting are currently declining, which will have a negative impact on farmers' economies by resulting in a low yield and make farmers less adept at predicting future crops. By utilizing machine learning, one of the most cutting-edge technologies in crop prediction, this research aids the novice farmer in a way that directs them towards sowing the reason- able crops. A supervised learning algorithm offers a strategy for doing so. Here, the appropriate parameters for temperature, humidity, and moisture content are collected along with the seed data of the crops, assisting in the successful growth of the latter. An important agricultural export commodity from Thailand, the Barracuda mango (Nam-Dok Mai), was the subject of this study on the development of an expert system for diagnosing plant diseases. Yet, due to Thailand's tropical climate, several plant diseases have been found there that have an impact on the survival of mango trees. Several different sorts of agricultural production are diminished when an agriculturalist is not aware of how plant illnesses should be categorized. Furthermore, there is no framework in place to suggest the best way to prevent or cure the illness that develops on their farm. As a result, their treatments for sick plants suffer substantially.
Key Words: Precision farming, machine learning, forecasting, categorization, detection, and plant diseases and pests.
