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Jambi Province is a producer of palm oil as a mainstay of commodities. However, the limited insight of farmers in Jambi to oil palm pests and diseases affects oil palm productivity. Meanwhile, knowing the types of pests and diseases in oil palm requires an expert, but access restrictions are a problem. This study offers a diagnosis of oil palm disease using the most popular concept in the field of artificial intelligence today. This method is deep learning. Various recent studies using CNN, say the results of image recognition accuracy are very good. The data used in this study came from oil palm image data from the Jambi Provincial Plantation Office. After the oil palm disease image data is trained, the training data model will be stored for the process of testing the oil palm disease diagnosis. The test evaluation is stored as a configuration matrix. So that it can be assessed how successful the system is to diagnose diseases in oil palm plants. From the testing, there were 2490 images of oil palm labeled with 11 disease categories. The highest accuracy results were 0.89 and the lowest was 0.83, and the average accuracy was 0.87. This shows that the results of the classification of oil palm images with CNN are quite good. These results can indicate the development of an automatic and mobile oil palm disease classification system to help farmers.

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Like most plantation plants in general, rubber can be attacked by various diseases originating from fungi, pests, animals and even cancer cells. For that we need a method capable of diagnosing rubber disease. In previous research related to the diagnosis of plant diseases, among others, using the Dempster Shafer method, the Certainty factor method and forward chaining. This study developed an analysis of the results of the diagnosis of rubber plant disease using the Mamdany Fuzzy method. The choice of this method departs from research on fuzzy mamdany which states that the fuzzy mamdany method is able to resemble the intuitive way the human brain works. It is hoped that with this method, the diagnosis of rubber plant disease can help farmers detect symptoms earlier so that the productivity of rubber plantation products can be achieved. increased. This study used rubber plant disease data from the Jambi Provincial Plantation Office in Jambi City. From the results of calculations carried out in diagnosing rubber plant disease, as many as 161 rubber plant object data were equipped with 33 symptom identities and a diagnosis from plantation data, then tested 60 rubber plant data without a diagnostic label, we obtained an accuracy value of 81.28%. Likewise, testing by randomizing training data with Cross Validation obtained close results.