Applying Machine Learning for Sustainable Farm Management: Integrating Crop Recommendations and Disease Identification

Context: The problems that contemporary farming methods confront can be greatly mitigated by using machine learning in sustainable agriculture. Combining methods for disease identification with crop recommendations allows farmers to make well-informed decisions that limit the effects of crop diseases on agricultural production while simultaneously increasing productivity. Objective: This article aims to provide a consistent method for diagnosing plant diseases and suggesting crop systems in agricultural contexts. The goal is to give farmers precise advice for the best crop choices and the prompt detection of plant diseases by utilizing machine learning algorithms. Materials/Methods: The utilization of Internet of Things (IoT) sensors, such as NPK and DT11 sensors, together with other environmental sensors, enabled the acquisition of data for this study. These sensors supply vital information on temperature, humidity, soil nutrients, and other environmental parameters that are critical for crop selection. To suggest appropriate crops and detect pertinent plant diseases, cutting-edge machine learning and deep learning algorithms were used. Real-time data from Internet of Things sensors and high-resolution camera photos were used to identify diseases. Plant diseases were accurately classified using state-of-the-art convolutional neural networks (CNNs), such as VGG16, ResNet50, and EfficientNetV2, based on visual signals including leaf color and texture. Results: Based on experimental data, a 99.98% accuracy rate was attained by the suggested recommendation system that used CNN. CNN, the illness identification system, attained an impressive 96.06% accuracy rate. It was then further implemented on cloud infrastructure, guaranteeing scalability and availability. The models’ performance was assessed using performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC; CNN showed an accuracy of almost 99.98%.