Enhancing Image Processing in Agriculture Through Machine Learning for Sustainable Crop Management

Currently, the need to adopt sustainability in the use of inputs in crop production is crucial in meeting out growing demand for food and assets and factors such as climate change and scarcity of resources. The purpose of this research is to analyze how these methods, such as machine learning and image processing, are effective in the agricultural field especially for efficiency, effective use of resources, and effects on the environment. Using complex image analysis techniques like CNNs for diseases and weeds as well as LSTM for yield estimate and Random Forest for the diseases risk estimate, the study proves how machine learning is useful in agriculture. The study shows that the yields of the crops increased by an average of 18%; water use, pesticide use and labor were successfully reduced. Furthermore, some more external effects like water runoff or chemical leaching dropped by about 30% which proves possible positive effects of the indicated technologies in terms of sustainability. In totality, this work offers a broad framework to SCN management and saw how artificial intelligence combined with image analysis results in optimal and eco-friendly farming.