Responsible AI-Assisted Crop Health Monitoring Using IoT Sensor Networks and Image Processing

Abstract

It is the need of the day to use AI in the agricultural sector in a responsible manner. It is necessary for a responsible and transparent approach, which can prove to be fruitful for the farmers. This paper also proposed a framework for Responsible AI Smart Farming based on environmental as well as visual analysis for crops. The solution proposed here is able to make effective use of environmental as well as visual factors. The solution proposed here is able to make effective use of environmental as well as visual factors, which include the use of environmental factors in the form of soil moisture, temperature, humidity, NPK, light intensity, infrared, as well as an ESP32 module camera. Being in line with the guidelines of Responsible AI, the system encourages human oversight, interpretability, and the use of recommendations over enforced outcomes. Methods of image processing, such as the evaluation of the color and vegetation indices, enable the automatic detection of the onset of a lack of nutrients, diseases, and stress, while the thresholds of the parameters sensed in the crops enable the development of context-aware outputs. The proposed approach has the capability of ensuring the promotion of sustainability in the agriculture sector by minimizing the usage of fertilizers and water and addressing the issue at the appropriate time. The proposed approach is a testament to the fact that ethically thought-out, inexpensive and ‘democratically’ available AI-assisted technologies are key for increasing agricultural productivity with accountability and social responsibility. This paper adds to the emerging Responsible AI literature in precision agriculture and provides a scalable framework with potential applications of more sophisticated advanced machine learning methods and farmer-centric decision support tools. [6] [1][3] [5] [2] [4]