Integrating Smart Farming with AI Techniques for Crop Disease Prediction

Le résumé fourni par la source

Farming encompasses the act of cultivating land, growing crops, and raising animals, which is crucial for the growth of country’s economy. For every country, agriculture contributes to approximately 70% of the main source of income. Historically, farmers have used traditional agricultural methods, which though time-consuming, often yielded imprecise results, leading to decreased output. Precision agriculture stands out as a viable solution to this issue, optimizing production by accurately identifying the necessary measures at an appropriate time. Elements of precision farming include meteorological prediction, soil assessment, crop recommendation for cultivation, and determination of optimal quantity of fertilizers and pesticides. Precise farming utilizes sophisticated techniques such as Machine learning, IoT, Data analytics and Data Mining to gather data, train systems, and forecast outcomes. Precision farming utilizes technology to minimize manual effort and enhance output. Recent agriculture challenges, such as crop failures because of low rainfall and soil sterility, raise the need for further exploration. This article aims to explore efficient methods used for crop management and harvesting in response to environmental changes. Several machine learning and deep learning models have come out with encouraging results in crop disease detection, thereby enhancing crop quality and productivity..

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