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Global agriculture is greatly impacted by plant diseases, which lower agricultural production and result in financial losses. Sustainable farming and successful intervention depend on early and precise detection. Conventional manual inspection techniques are labor-intensive, sluggish, and prone to mistakes. Because AI and ML provide quicker, more accurate, and scalable solutions, they have revolutionized the detection of disease. Plant pictures are analysed by machine learning models such as CNNs, logistic regression, SVM, Naive Bayes, and decision trees to automatically classify diseases. While other models increase efficiency, CNNs increase detection accuracy by recognizing complex visual cues. Additionally, AI-powered systems prescribe fertilizer depending on environmental variables, crop kind, and soil health. By assisting farmers in making data-driven decisions, this integrated method raises production. AI in agriculture promotes sustainable farming, increases productivity, and reduces losses. Modern agriculture is being reshaped by AI-powered technologies to increase yields and resilience. Our web-based tool diagnoses plant illnesses and makes fertilizer recommendations using machine learning and deep learning. In addition to decision trees, Naive Bayes, SVM, logistic regression, and XGBoost for precise classification, it uses CNNs for image-based disease diagnosis. Personalized and fertilizer recommendations are given by the system according on crop type, soil condition, and disease status. Agricultural decision-making is made more accurate and efficient by combining several models. The strategy aids farmers in implementing sustainable farming methods and increasing crop yields. All things considered, the model encourages precision farming powered by AI for increased resilience and productivity. This might lead to increased crop yields and more sustainable farming practices. This research underscores the potential of machine learning-driven decision support systems in enhancing agricultural productivity and resilience.

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Plant Disease Detection and Crop Recommendation System

  • 会议论文

  • 发表日期：

    2026年

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  • Tarun Kumar Gautum,
  • Aditya Gupta,
  • Aryan Gupta,
  • Aryan Singh,
  • Ayush Sharma

摘要

Global agriculture is greatly impacted by plant diseases, which lower agricultural production and result in financial losses. Sustainable farming and successful intervention depend on early and precise detection. Conventional manual inspection techniques are labor-intensive, sluggish, and prone to mistakes. Because AI and ML provide quicker, more accurate, and scalable solutions, they have revolutionized the detection of disease. Plant pictures are analysed by machine learning models such as CNNs, logistic regression, SVM, Naive Bayes, and decision trees to automatically classify diseases. While other models increase efficiency, CNNs increase detection accuracy by recognizing complex visual cues. Additionally, AI-powered systems prescribe fertilizer depending on environmental variables, crop kind, and soil health. By assisting farmers in making data-driven decisions, this integrated method raises production. AI in agriculture promotes sustainable farming, increases productivity, and reduces losses. Modern agriculture is being reshaped by AI-powered technologies to increase yields and resilience. Our web-based tool diagnoses plant illnesses and makes fertilizer recommendations using machine learning and deep learning. In addition to decision trees, Naive Bayes, SVM, logistic regression, and XGBoost for precise classification, it uses CNNs for image-based disease diagnosis. Personalized and fertilizer recommendations are given by the system according on crop type, soil condition, and disease status. Agricultural decision-making is made more accurate and efficient by combining several models. The strategy aids farmers in implementing sustainable farming methods and increasing crop yields. All things considered, the model encourages precision farming powered by AI for increased resilience and productivity. This might lead to increased crop yields and more sustainable farming practices. This research underscores the potential of machine learning-driven decision support systems in enhancing agricultural productivity and resilience.