An efficient deep-learning model for olive tree diseases diagnosis in Al-Jouf region

Olive trees are considered one of the most important crops in agriculture, providing crucial economic and ecological benefits worldwide. They are, however, extremely susceptible to various diseases, causing heavy losses in yield and quality if they are not identified early. Traditional methods, including visual inspections and laboratory testing, are time-consuming, expensive, and sometimes inaccurate, necessitating the need for automated and efficient solutions. This study overcomes these hurdles by designing an olive tree disease detection Deep Learning (DL) system that leverages the power of EfficientNet-Futuristic and Meta-Futuristic Algorithm Optimization for Olive Tree Disease Detection. A feature extraction mechanism that exploits CNNs alongside a decentralized multi-agent framework utilizes reinforcement learning. This offers a method that can carry out the detection of olive tree diseases at any time during vast plantations, allowing disease management systems to attain real-time improvement both in accuracy and in scale. The EfficientNet-Futuristic and Meta-Futuristic Algorithm Optimization model will optimize feature extraction processes and the classification procedure, leading to an accuracy of about 99.4 %, effectively identifying diseases like Aculus Olearius, Olive Peacock Disease, as well as Leaf Scab. Reinforcement learning enables interaction with multiple agents in cooperative behavior, resulting in enhanced adaptability capabilities. While other studies have used CNNs and reinforcement learning in agriculture, this approach introduces a new way to fine-tune the parameters of the CNN as well as the actions of the agent through dual-stage optimization involving Futuristic (PSO-like) and Meta-Futuristic (GA-like) techniques. Moreover, we develop a multi-agent reinforcement tool using segmentation, classification, and coordination, which allows disease diagnosis in UAV-monitored orchards at scale. Most agricultural disease research does not use this holistic architecture approach. The results showed that the proposed system is superior in accuracy, precision, and recall as compared to traditional methods and has given a reliable and scalable solution for the detection of diseases in olive trees. The research would be fruitful for farmers because it provides a high-accuracy tool for early detection and management of diseases, ultimately reducing crop loss and increasing productivity in farms. Moreover, this is also a contribution towards agricultural AI as it has portrayed DL and multi-agent systems as driving forces toward sustainable farming practices.