Abstract: Identification of plant diseases is the most important part of agriculture. We take this into consideration as it has a direct impact on the production of the crops. Early detection of the diseases helps in preventing the failure of the crops. Turmeric, which is a rhizomatous crop, is very popular and it's known for its medical, culinary, and non-culinary uses. Usually, a plants get affected due to various diseases, among which rhizome rot is most important as it leads to the major downfall in the turmeric crop, and this creates a huge impact on the farmers at the rate of 60%. To analyze the severity of the nature of this disease, a database with a distinct PH value was generated and analyzed with the help of various algorithms on the turmeric plant. The machine learning algorithm is utilized for the purpose of detecting and preventing this disease. Then the results of various machine learning algorithms are tabulated and analyzed for the optimum algorithm yielding the maximum outcome. This method achieves outstanding accuracy of approximately Gradient Boosting Machines produced marginally superior outcomes with 97.7% accuracy, 98.75% precision, 93.92% recall, and 94.71% F1-Score using their iterative refining technique. GBM's superior metrics stem from its proficiency in sequential model construction, which enables it to capture intricate relationships and interactions. Both models provide reliable predictions for disease classification, which greatly improves agricultural decision-making.

Keywords: Agriculture Plant Diseases, Diseases Early Detection, Turmeric Crop, Machine Learning, Gradient Boosting, Sequential Classification, Rhizome Rot

Pages: 400-414