The production of crops is a key factor in any nation’s economic growth. Diseases have a significant negative impact on plant output, lowering the quality of the plants and costing farmers money. Different sections of a plant exhibit symptoms of plant diseases, however, leaves are the most frequently seen area for spotting an infection. Extensive studies are conducted to perform precision agriculture and aid in the early diagnosis of plant diseases. The model tries to apply more recent bio-inspired algorithms for effective feature selection, which can improve the efficiency and accuracy of the classifier's training. Overall, we can identify the illness present in plants on a massive scale by utilizing machine learning to train the vast data sets that are publicly available. Our findings demonstrate that the analysis of obtained results is not only efficient but also contributes to robust predictive analysis. This affirms the dependability and effectiveness of our proposed hybrid model in precisely diagnosing plant leaf diseases.