International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
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Using AI to Recommend Pesticides for Effective Management of Multiple Plant Diseases Shreyanshi Patel1, Rahul Borkar2, Adarsh Kumar3, Kunal Raj4, Aashish Sahu5 1Professor, Priyadarshani College of Engineering, Nagpur, Maharastra 2Under Graduate Student, Priyadarshani College of Engineering, Nagpur, Maharastra
-------------------------------------------------------------------***----------------------------------------------------------------------by helping to ensure soil fertility, pollination and pest Abstract
control. For these reasons, agriculture is key for producing food for a growing world population [5].
Trees, Plants and Crops are one of the principal sources of food for humans as well as other animals. They are crucial for our continuance. Similar to us they are also living organisms. Once in a while we get afflicted by diverse diseases. Like us, plants are also affected by various types of illness. Plants that are infected by disease have results on their health which have severe consequences like less food production. Most plant ailments are contagious which spread rapidly all over the whole crop. Prior prevention and ceasing of disease is a necessity step to stop further harm and proper crop production. Usually, farmers or professionals keep a close eye on the plants in order to discover and identify diseases. However, this procedure is frequently time-consuming, costly, and imprecise. We need to ameliorate and quicken the process of disease perception and its diagnosis. The main aim of this research paper is to demonstrate a Disease Recognition System that is supported by providing solutions with Fertilizer Recommendation to make plant disease spotting easier and briskly. In this research paper we are providing methodology to make use of Computer Vision with a Machine Learning Model (Convolution Neural Network) to make an effective system for plant disease detection. CNN is a form of artificial neural network that is specifically intended to process pixel input and it is used in image recognition. Overall, we are intended to provide a method using machine learning to detect the disease present in plants on a colossal scale.
How do plant diseases impact food security? Plant diseases are a major impediment to the production and quality of important food stuff. Pests and diseases pose a threat to food security Because they can damage crops, thus reducing the availability and access to food, increasing the cost of food. In addition, plant disease can devastate natural ecosystems, compounding environmental problems caused by habitat loss and poor land management. The most direct economic impact of a trans boundary pest or disease is the loss or reduced efficiency of agricultural production - whether it be of crops or animals - which reduces farm income. The severity of the economic effect will depend on the specific circumstances [5]. Independent of the prevention approach, identifying a disease correctly when it first appears is a crucial step for efficient disease management. Majority farmers based on their experience and knowledge try to identify plant disease and try to prevent it by applying pesticides or fertilizers on the farm. But this is not an accurate method and the wrong prevention approach might of course damage the crops. Disease identification and solution has been supported by agricultural extension organizations or other institutions, such as local plant clinics. But the cost of the process is high and most clinic labs are located in the city which makes it difficult for farmers.
Keywords - Convolutionla Neural Network (CNN), Colossal, Computer Vision, Disease.
A system capable of performing such tasks can play an important role in avoiding the excessive use of pesticides and chemicals, reducing both the damage caused to the environment and to the associated use of pesticides and chemicals. The growing technology in machine learning and availability of big data analysis methods has the potential to spur even more research and development in smart farming. Besides promoting higher yield crops in a more sustainable manner, it also aims to contribute to event forecasting, detection of diseases, and management of farms.
Introduction Every day, agriculture produces an average of 23.7 million tons of food, provides livelihoods for 2.5 billion people, and it is also the largest source of income and jobs for poor, rural households. In developing countries, agriculture accounts for 29% of GDP and 65% of jobs. The different pet animal breeds, birds and insects also directly or indirectly depend upon agricultural food for their aliment.. In addition, biodiversity directly supports agriculture systems
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