Integrated Framework for Enhanced Carrot Disease Classification: CNN and Random Forest Integration

The purpose of this research is to construct a classification model that is both reliable and accurate by using the most advanced machine learning methods available for detecting illnesses that damage carrots. Through the use of Convolutional Neural Networks (also known as CNNs) as well as Random Forest techniques, the model demonstrates remarkable effectiveness in identifying a collection of illnesses that are connected with carrots. Outstanding accuracy rates, ranging from 92.65% to 95.04%, are highlighted by the assessment measures, which are applied to a variety of diseases, including Black Root Rot, Cavity Spot, Common Scab, Sclerotinia Rot - White Mold, as well as Lateral Root Dieback. The ability of the algorithm to dependably retrieve relevant samples under a variety of sickness categories is an exceptionally intriguing aspect of the approach. The simple fact that the memory scores run from 92.31% to 95.17% shows that this is a real skill. These three averages-the macro, the weighted, and the micro-are very important because they show that the method works well and reliably to group diseases over time. This is how they choose to act because it shows that the plan is working. The data shows that the mean (weighted) value is about 94.14%, the micro average value is about 94.85%, and the macro median value is about 94.12%. It can be seen from these numbers that the system stays stable when measuring it in different ways. The weighted mean is 94.14%, and the micro average is 94.85% in this case. According to the results of this thorough evaluation, the program is capable of accurately identifying and classifying a wide range of diseases that are linked to carrots. Just the fact that the model can do this shows that it is possible. Not only does this model have important effects for study, but it could also be used to test things automatically. Because of this feature, finding diseases in carrot areas would be easier and better. The way that study is done changes because of this idea.