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Leaf blight, caused by bacterial and fungal infections, threatens plant health by inducing leaf discoloration and reducing crop yield. To address this, we propose a MobileNetV3-based classifier for the Jasmine plant, leveraging streamlined Convolutional Neural Networks (CNNs) to precisely identify disease stages. The design combines depthwise convolutional layers with max-pooling layers to extract superior features, focusing on vital low-level features that signal the condition. Via preprocessing methods that include data augmentation with a Conditional GAN and Particle Swarm Optimization for feature selection, the classifier achieves reliable performance. Assessment on carefully assembled datasets shows a remarkable 97% training accuracy, demonstrating its potency. Real-world trials across varying settings, including severe camera tilts and fluctuating illumination, verify the model’s durability, delivering test accuracies within the 94% to 96% range. The dataset’s bespoke structure for CNN-based classification ensures the reliability of the outcomes. Notably, the model’s compact classification, characterized by swift processing speed and minimal size, makes it a practical choice for immediate deployment. This all-encompassing strategy highlights the presented classifier’s value in managing leaf blight spot disease difficulties in cash crops.

Intelligent agriculture increasingly incorporates automation, artificial intelligence, robotics, sensors, digital farm platforms, remote sensing, and predictive analytics into everyday farm management. These systems are frequently presented as tools for improving efficiency, precision, productivity, and labour allocation. Yet their value cannot be assessed only through technical performance or economic output. Their deeper significance lies in how they reshape the relationship between farmers, knowledge, technology, and agronomic decision-making. A central risk is that automation may substitute for farmer expertise rather than support it. When digital systems hide decision logic, prescribe action without explanation, or reduce farmers to passive operators, they can weaken practical skill, situated judgment, and opportunities for learning. Such effects are especially important in agriculture, where decisions are made under ecological variability, uncertainty, and local specificity. Intelligent systems therefore require evaluation not only as tools of control, but also as environments that can either preserve or erode human capability. This article develops a human-centred conceptual model for shifting from automation-oriented replacement toward augmentation-oriented support in agriculture. The model is organised around three connected capability domains: skill preservation, farmer judgment, and agronomic learning. It argues that intelligent agricultural systems become augmentative when they extend perception, improve interpretation, support deliberation, enable feedback, and preserve meaningful farmer control. The model also identifies design principles and boundary conditions that determine whether digital systems strengthen or weaken human capability. The article contributes a structured way of assessing intelligent farming beyond labour substitution, prediction accuracy, or platform efficiency. It proposes that digital agricultural systems should be judged by whether they help farmers notice more, reason better, decide more reflectively, and learn more effectively from farm outcomes. This framing positions augmentation as a human-centred design and governance challenge, not merely a technical feature. Agricultural intelligence is therefore understood as a shared capability between people, technologies, advisory systems, and situated agronomic practice.