Harvesting Knowledge: Data Science and Machine Learning Techniques for Evaluating Pesticide Impact in Vegetable Organic Farming

Abstract

Abstract: The integration of data science and machine learning is revolutionizing the assessment of pesticide impact in organic vegetable farming. This review explores methodologies, applications, and research examples showcasing the transformative potential of data-driven approaches. Remote sensing, including satellite imagery and drones, is essential for monitoring crop health and detecting pesticide impacts on vegetable crops like tomatoes, lettuce, and red peppers. By synthesizing research and trends, the review underscores technology's significance in informed decision-making for sustainable vegetable organic farming practices. Spectral analysis and vegetation indices quantify changes in crop health, informing pesticide efficacy and environmental impact. Sensor networks and IoT devices allow real-time monitoring of environmental conditions and pesticide dynamics, optimizing application practices to minimize contamination while maximizing yield. Machine learning, particularly decision tree-based models like random forests, predicts and mitigates pesticide impacts by analyzing complex datasets. Incorporating variables such as soil type and climate, these models accurately forecast pesticide fate, aiding in targeted mitigation strategies. Deep learning, such as convolutional neural networks (CNNs), identifies pesticide stress symptoms from digital images of vegetable leaves, facilitating rapid intervention. Challenges like data integration and model interpretability persist, yet ongoing research addresses these through data fusion and explainable AI. This review emphasizes the progress in leveraging data science and machine learning for pesticide impact evaluation in organic vegetable farming. By synthesizing research and trends, it offers insights for future sustainable agriculture applications.