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In our modern society, agriculture plays a vital role in meeting our food needs while minimizing environmental risks and crop issues at every stage possible through sustainable farming practices adopted by farmers worldwide today. However, traditional methods cannot meet these criteria due to weather changes, soil characteristics impacting crop growth, diseases causing crop loss, or reduced productivity hurting farmers' income. To address these issues, we introduce an innovative Integrated Agriculture System that utilizes real-time weather data and IoT sensors to optimize crop management, going beyond traditional methods. The system incorporates machine learning models trained on environmental and soil parameters to predict crop type and fertilizer needs, enabling informed decisions for sustainable farming. Additionally, the system features a plant disease detection tool that captures crop photos and identifies whether a crop is infected or not; based on that, the system provides recommendations. Our study demonstrates exceptional predictive capabilities, with Naive Bayes and Random Forest achieving accuracy scores of 0.98 % and 0.99 %, and Logistic Regression and Decision Tree performing well with accuracies of 0.95% and 0.94%. These results highlight the system's effectiveness in enhancing productivity while minimizing environmental risks, making it an indispensable tool for modern and sustainable agriculture.
Rahman et al. (Thu,) studied this question.