Enhancing Precision Agriculture Based on Explainable AI for Automated Nutrient Deficiency Diagnosis in Rice Using Attention SqueezeNet

Abstract

Rice (Oryza sativa) is a linchpin of global food security, feeding most of the world’s population, especially in Asia and Africa. Nitrogen (N), Phosphorus (P), and Potassium (K) must be held in precise equilibrium for rice to grow right and produce well. A lack of these nutrients can put plant health at risk, causing poor yields that result in huge economic losses to farmers. This means it is important to have an accurate diagnosis done on time so that corrective measures can be taken to ensure the sustainability of rice cultivation. Conventional techniques used to detect nutrient deficiencies in rice plants like manual inspection and biochemical testing are frequently inadequate. Manual inspection may be effective, but it is laborious, subjective, and unworkable when working on a large-scale farm. On the other hand, the accuracy of biochemical tests does not compensate for their time-consuming nature, their cost as well as the need for specialized equipment and expertise; this makes them inaccessible to many smallholder farmers. This research proposes Attention SqueezeNet-a streamlined deep learning model aimed at overcoming such limitations. Attention SqueezeNet leverages the Kaggle “Nutrient Deficiency Symptoms in Rice” dataset by including an attention mechanism that focuses on significant visual features associated with N P K deficiency on rice leaves. Compared with traditional methods, therefore, these focused areas enable more accurate diagnostic results from the model as opposed to conventional methods which diagnose diseases broadly rather than focusing on specific symptoms. In this context, we propose: i) developing a robust Attention SqueezeNet model specifically aimed at categorizing nutrient deficiencies in rice plants; ii) making the model more robust against unseen data variations through pre-processing and augmenting existing dataset; iii) comparing the performance of Attention SqueezeNet with existing deep learning models in terms of accuracy and efficiency per computational unit utilized (CU). The results show that Attention SqueezeNet has better classification accuracy compared to state-of-the-art models and is more computationally efficient than them. This has resulted in the transformation of agriculture through automation and objectivity in diagnosis of nutrient deficiency which ensures sustainable crop management practices towards global food security.