Using Mobile Edge AI to Detect and Map Diseases in Citrus Orchards

Abstract

Deep Learning models have presented promising results when applied to Agriculture 4.0. Among other applications, these models can be used in disease detection and fruit counting. Deep Learning models usually have many layers in the architecture and millions of parameters. This aspect hinders the use of Deep Learning on mobile devices as they require a large amount of processing power for inference. In addition, the lack of high-quality Internet connectivity in the field impedes the usage of cloud computing, pushing the processing towards edge devices. This work describes the proposal of an edge AI application to detect and map diseases in citrus orchards. The proposed system has low computational demand, enabling the use of low-footprint models for both detection and classification tasks. We initially compared AI algorithms to detect fruits on trees. Specifically, we analyzed and compared YOLO and Faster R-CNN. Then, we studied lean AI models to perform the classification task. In this context, we tested and compared the performance of MobileNetV2, EfficientNetV2-B0, and NASNet-Mobile. In the detection task, YOLO and Faster R-CNN had similar AI performance metrics, but YOLO was significantly faster. In the image classification task, MobileNetMobileV2 and EfficientNetV2-B0 obtained an accuracy of 100%, while NASNet-Mobile had a 98% performance. As for the timing performance, MobileNetV2 and EfficientNetV2-B0 were the best candidates, while NASNet-Mobile was significantly worse. Furthermore, MobileNetV2 had a 10% better performance than EfficientNetV2-B0. Finally, we provide a method to evaluate the results from these algorithms towards describing the disease spread using statistical parametric models and a genetic algorithm to perform the parameters’ regression. With these results, we validated the proposed pipeline, enabling the usage of adequate AI models to develop a mobile edge AI solution.

Deep learning approaches for object recognition in plant diseases: a review

“…Some research uses their citrus disease dataset to make comparisons between one-stage and two-stage methods [55,58] . While most researchers pay attention to deep learning models with large amount parameters, da Silva et al build some efficient mobile networks aiming at implementing real-time detection on smartphones [59] .…”

Inteligencia artificial en agricultura - Uso de algoritmos, drones y biosensores

“…Los modelos de aprendizaje profundo (AP) han presentado resultados prometedores cuando se aplican a la agricultura de precisión 4.0. Entre otras aplicaciones, estos modelos se pueden utilizar en la detección de enfermedades y el conteo de frutas en huertos de cítricos (da Silva et al, 2023). Los modelos de aprendizaje profundo suelen tener muchas capas en la arquitectura y millones de parámetros.…”

Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI

“…One such technique is the automatic detection and identification of plant diseases using deep learning models and image processing methods 22 . In recent times, AI-based deep learning models have revolutionized plant disease diagnosis, as researchers are increasingly using deep learning models to identify and categorize plant diseases in major crops 23 , 24 , including Rice 25 – 27 , Wheat 28 , 29 , Maize 30 , Tomato 31 – 34 , Banana 16 , 35 , Apple 34 , 35 , Grapes 15 , 36 , Citrus 37 – 39 , Mango 40 , 41 , Tea 42 – 44 , Cucumber 45 , 46 , Cassava 47 , 48 , Ginger 18 , 47 , Sugarcane 48 , 49 , Papaya 50 , and Pearl Millet 51 . Despite their effectiveness in diagnosing and classifying various crop diseases, these techniques still encounter limitations in detecting and recognizing common bean diseases in real-field environments.…”