Abstract:
Farmers are facing many difficulties right from the selection of seed to fertilizer usage, disease control, harvesting and selling the
agricultural yield. The prime motivation behind this research stems from the idea that, the ability to detect leaf issues and implement
corrective measures can offer a solution to mitigate the decrease in crop productivity. The existing Deep Learning methods like
Convolutional Neural Network showed high efficiency regarding the modification and use of acquired knowledge. A novel
framework has been developed by incorporating Convolutional Neural Network and tuning the hyperparameters. Training has been
performed using Extreme Learning process which yielded better results. Convolutional Neural Network - Extreme Learning
Algorithm is the underlying algorithm. The empirical study makes use of the Plant Village dataset. The leaf disease categories
considered in this research early blight, black rot, bacterial spot, apple scab, cercospora leaf spot and healthy. Convolutional Neural
Network - Extreme Learning achieved 94.28% precision, 95.63% accuracy, 94.68% recall, and 96.23% F1-score using Plant Village
dataset, outperforming other classifiers. The research outcomes reflect that the proposed Deep Learning model and algorithm can be
used real world computer vision applications pertaining to agriculture.
