Crop Weed Prediction and Smart Crop Yielding

Abstract

Machine learning is used to identify crop weeds in the project "Crop Weed prediction and smart crop yields." In India, agriculture is one of the most significant and old professions. The production of food must be handled with the utmost care because agriculture is the foundation of India's economy. Plants become infected by weeds like viruses, fungi, and bacteria, which results in decreased output of both quality and quantity. Farmers are leaving the agricultural industry in large numbers. Therefore, taking good care of plants is essential for the same. Image processing offers more effective approaches to find weeds on plants that are brought on by fungus, bacteria, or virus. Weed detection by merely using the eyes is ineffective. The quality of plant nutrients is also harmed by overuse. Farmers suffer a significant loss in production as a result. Therefore, it is useful to apply image processing techniques to identify and categorize weeds in agricultural applications. The agriculture industry has great potential to reduce food shortages and supply wholesome, nutritious food. Farmers face a difficult problem when trying to identify crop weeds since weed invasion causes significant crop loss and quality degradation. The disadvantage of traditional weed identification is that it requires skilled taxonomists to correctly identify weeds based on physical characteristics. In order to identify crop weeds early on and shorten the time needed to improve crop production and crop quality, classification and prediction accuracy findings are used.