Classifying Crop Leaf Diseases using Different Deep Learning Models with Transfer Learning

Within the scope of the research, we put forward a technique of exactly confirming the distinctiveness of agricultural leaf pathologies with the assist of deep mastering algorithms and switch getting to know generation. The contribution of this work to the development of reliable systems of save you sicknesses in production touches upon the rural exercise to achieve superiority fits into precision agriculture and sustainable farming.

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• 1 We have pre-skilled models like VGG19, MobileNet, InceptionV3, EfficientNetB0, Simple CNN where we are seeking to increase the utility for the crop disorder type.
• 2 Through searching at some metrics as cited Accuracy, Precision, Recall and F1 score for a better knowledge of a crop leaf photo category, we observe how each version performs.
• 3 Our paper shows that artificial intelligence is fairly useful for the obligations of the automatic disease detection and switch mastering (as a method for reusing the existing understanding in the new software) is also beneficial.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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