Fruits come in different variants and subspecies. While some subspecies of fruits can be easily differentiated, others may require an expertness to differentiate them. Although farmers rely on the traditional methods to identify and classify fruit types, the methods are prone to so many challenges. Training a machine to identify and classify fruit types in place of traditional methods can ensure precision fruit classification. By taking advantage of the state-of-the-art image recognition techniques, we approach fruits classification from another perspective by proposing a high performing hybrid deep learning which could ensure precision mangosteen fruit classification. This involves a proposed optimized Convolutional Neural Network (CNN) model compared to other optimized models such as Xception, VGG16, and ResNet50 using Adam, RMSprop, Adagrad, and Stochastic Gradient Descent (SGD) optimizers on specified dense layers and filters numbers. The proposed CNN model has three types of layers that make up its model, they are: 1) the convolutional layers, 2) the pooling layers, and 3) the fully connected (FC) layers. The first convolution layer uses convolution filters with a filter size of 3x3 used for initializing the neural network with some weights prior to updating to a better value for each iteration. The CNN architecture is formed from stacking these layers. Our self-acquired dataset which is composed of four different types of Malaysian mangosteen fruit, namely Manggis Hutan, Manggis Mesta, Manggis Putih and Manggis Ungu was employed for the training and testing of the proposed CNN model. The proposed CNN model achieved 94.99% classification accuracy higher than the optimized Xception model which achieved 90.62% accuracy in the second position.

The pepper plant is considered a valuable and popular choice of crop in Malaysia despite market fluctuation affecting its prices and other constraints in the pepper industry. In addition, pepper plant is mainly grown in rural areas of Sarawak and the growers are mainly smallholders who face tough challenges in the industry. Thus, this research endeavours to explore the challenges faced by pepper growers in Sarawak as seen from their perspectives. Qualitative data with a phenomenological approach was conducted to allow an in-depth understanding of their challenges. Data analysis of six semi-structured interviews on the pepper growers was conducted at an agreeable place to the participants. A sampling method was done via snowball sampling. The findings indicated several main themes of the challenges. The emerging main themes revealed that the challenges were associated with a shortage of workers, climate, profits, government assistance, pest, and crop diseases. The result will provide useful information for the government authority, agricultural agencies, and help to formulate the action plan to enhance pepper cultivation.