article · Ecological Informatics
Plant diseases cause significant agricultural produce loss, harming economies and food security. Traditional diagnosis relies on visual inspection by experts or farmers, which suffers from low accuracy and human dependence. To address this, an automated approach combining deep learning architectures, specifically DenseNet201, EfficientNetB0, InceptionResNetV2, and EfficientNetB3, has been developed to classify plant leaf diseases. The workflow incorporates a novel image-processing method to improve model efficiency alongside a data-balancing technique for imbalanced data. Evaluated on the PlantVillage dataset across 38 classes, ten randomly selected model ensembles were tested. The top-performing ensemble reached a classification accuracy of 99.89 percent, demonstrating superior accuracy and F1-score compared to existing state-of-the-art models in the literature.
Plant diseases reduce crop yields, threatening global food security and economic stability. Traditional visual identification by farmers or specialists is constrained by human error and limited expert availability. Automating disease detection with near-perfect diagnostic accuracy can support timely interventions, offering a reliable tool to protect harvest yields without relying entirely on manual visual inspection.
The technology represents an automated diagnostic system for classifying crop diseases, which could assist farmers, agronomists, and agricultural extension workers. Evaluated on an established benchmark dataset covering 38 classes, the system demonstrates high laboratory accuracy. However, because the abstract only describes testing on benchmark dataset imagery, the technology is currently at an applied and tested research stage rather than deployed in live agricultural environments.
AI-generated from the published abstract. Always read the original work before citing.
A substantial fraction of agricultural produce loss can be attributed to plant diseases; agricultural yield loss can have far-reaching consequences for a country's economy and contribute to global food insecurity. Early detection of plant diseases can be instrumental in maintaining global health and welfare. A pathologist's visual evaluation is typically used to make an early diagnosis of plant diseases. This technique involves experts or farmers examining plants with the naked eye and classifying the disease depending on their previous experience. This conventional approach includes drawbacks like low accuracy and the need for human expertise. This motivates researchers to investigate automated systems for the early diagnosis of plant diseases. To achieve this goal an ensemble of different deep learning architectures (DenseNet201, efficientNetB0, inceptionresnetV2, efficientNetB3) is introduced to increase the classification accuracy of plant leaf diseases. In this work, a novel image-processing technique is proposed to increase the efficiency of deep-learning models. Also, a data balancing technique is used to solve the problem of the imbalanced dataset. Five different deep-learning models are trained and tested using the largest plant disease dataset; PlantVillage. Ten different ensembles (chosen randomly) of the deep learning models are tested and compared to find the ensemble with the highest accuracy. The proposed ensemble model was able to achieve 99.89% accuracy on the New PlantVillage dataset. PlantVillage is a challenging dataset with 38 classes. Achieving high accuracies on such a dataset proves the ability of the system to generalize on unseen data or real-world scenarios. A comparison with the state-of-the-art is made with other available models from the literature. A section about this is added to show the superior performance of the proposed ensemble model in terms of accuracy and F1-score.
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DOI: 10.1016/j.ecoinf.2024.102618
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