The objective of the present research is to investigate the fusion of quantum neural networks (QNNs) and ant colony optimization (ACO) strategies in order to optimize agricultural predictions and as a result improve the precision of decision-making processes. Specifically, focusing on optimizing the crop yield and efficiency of resource utilization, our research explores the capability of QNN to predict the outcomes of agricultural decisions as a part of the sensed parameter and the amount of fertilizers provided. Similarly, to a variety of computational research, the massive potential of quantum-inspired computational strategies is reflected in their extraordinary accuracy. Throughout our research efforts and applying QNN as an instrument of prediction, the latter composes 96.76% of the crop yield rate. Moreover, QNN in combination with ACO proved to be an acceptable response to optimization strategies aimed at sensor parameters and fertilizers, reaching the outstanding rate of 98.23%. In turn, the present research adheres to the transparent evaluation principles and compares the current performance of QNN with the known machine learning models, such as artificial neural networks, logistic regression, and random forest. Our results prove the superiority of QNN based on variable proofs and evaluation procedures and claim to the performative quality of quantum-inspired strategies and optimization techniques as the means of rethinking the principles of agricultural decision support in the context of agricultural sustainability and optimal plant productivity. Thus, the research perspectives enable a description of the potential implications of quantum strategies on enhanced agricultural technologies. These result in the substantial value of current research, as they enable applications of quantum-inspired technologies to computational research aimed at optimizing agricultural systems.