Comparative evaluation of deep learning architectures for brinjal fruit disease classification

Micronutrient malnutrition, especially anaemia, remains a pressing global concern, underscoring the value of affordable and nutrient-dense crops such as brinjal ( Solanum melongena L.). Yet, its cultivation is hampered by fruit diseases that significantly diminish both yield and market quality. Conventional diagnosis depends on manual inspection, which is time-consuming and prone to error. To overcome this limitation, we conducted a comprehensive benchmarking of contemporary deep learning architectures for automated brinjal fruit disease recognition under real-world field conditions. For this purpose, we developed the BrinjalFruitX dataset containing 3,077 images of five classes—Healthy, Phomopsis Blight, Wet Rot, Shoot and Fruit Borer, and Fruit Cracking—captured under natural variability. We evaluated ten representative convolutional neural networks (CNNs), including models from the VGG, ResNet, Inception, EfficientNet, and MobileNet families, across four training paradigms: training from scratch, transfer learning, fine-tuning, and full training (“Full Monty”). Performance was systematically analyzed using accuracy, macro- and weighted-F1 scores, training loss, confusion matrices, and computational efficiency indicators such as parameter count, FLOPs, and inference latency. Among the tested models, MobileNetV2 with Full Monty training achieved the best balance of performance and efficiency, reaching 97.98% accuracy, a macro-F1 of 0.9793, and operating with only 3.4M parameters, 0.30B FLOPs, and an inference time of 3.2 ms per image. While InceptionV3 and VGG16 also produced competitive results, they required considerably higher computational resources. In contrast, deeper ResNets and EfficientNetB0 offered inferior accuracy despite higher complexity. These findings highlight MobileNetV2 with full training as a practical and lightweight solution for on-device and farmer-oriented applications. This study establishes a strong benchmark for advancing deep learning–driven disease detection in sustainable agricultural systems.