Developing efficient and reliable methodologies for assessing and forecasting the root yield of Cassava/Tapicoca might minimise the time and effort required to phenotype complex variables related to productivity and abiotic stress. To find phenotypic factors highly associated with Fresh Root Yield (FRY) and to construct a model to predict genotype performance under water shortage conditions. One of the most challenging problems confronting agricultural specialists throughout the globe in recent years has been automating the diagnosis of numerous plant diseases. In this review, to analyze the tapicoca yield prediction and disease identification was analyzed. Cassava Brown Steak Disease (CBSD), Cassava Green Mite (CGM), Cassava Bacterial Blight (CBB), and Cassava Mosaic Disease (CMD) and health impacts are employed in this investigation for earliest possible detection of the disorders. We collect 26 recent papers for survey in disease detection in cassava plants based on a Neural Network (NN). Neural Networks will automatically learn the characteristics in cassava plant images that indicate the presence of illness

Cassava, Disease Identification, Disease Prediction, Yield Prediction