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Oil palm plants are essential as they produce palm fruit that can be processed into edible oil—an essential human need. However, these plants are often infected with diseases, negatively impacting crop productivity and the quality of the oil produced. These diseases are caused by mushrooms, bacteria, viruses, and pests that can spread rapidly and damage the leaves. Therefore, early detection of oil palm leaf disease plays a crucial role in reducing the negative impact on crops and significant economic losses. This study aims to design a system to classify the types of leaf diseases of oil palm plants using texture feature extraction (Haar Wavelet Algorithm) and machine learning-based classification algorithms (Cubic SVM, Medium Gaussian SVM, Quadratic SVM, Cosine KNN, Fine KNN, and Weighted KNN). Cubic SVM yielded the highest training result with an averages accuracy of 81.54% and an average time of 48.135 seconds. However, Medium Gaussian SVM outperformed other models during testing, producing an accuracy of 87%, precision of 81%, recall of 81 %, specificity of 90%, and F-score of 81%.

Pepper (Piper nigrum L.) is a high-value cash crop and plays a significant role in Indonesia's agricultural sector. However, pepper production is often hindered by diseases that affect the plant's leaves. This study aims to develop a pepper leaf disease detection model based on image analysis using a Radial Basis Function Neural Network (RBFNN). Conventional methods relying on expert visual assessment are often inefficient, especially on a large scale. In this research, image preprocessing was performed by transforming the images into the CIELAB color space and using K-Means Clustering for feature extraction. Texture feature extraction using the Gray Level Co-occurrence Matrix (GLCM) provides rich information about patterns and intensity distribution in the images, which is effective for distinguishing disease classes. The RBFNN algorithm is then used to identify diseases by capturing the complex non-linearities in the data. Based on the testing results, this model achieved an accuracy rate of 91.67%, demonstrating excellent performance.

In recent years, IoT has become a reliable technology in the agricultural sector to optimize cultivation results. In the Marsudi Luhur breeder group, the harvest results were not optimal because many catfish died and gave off a bad smell. According to several references, to ensure healthy growth of catfish, continuous irrigation of the pond is required even though the flow rate is low. The location of the catfish pond is quite far from residential areas so it is not practical to monitor irrigation performance by frequently visiting the pond area. Therefore, this project aims to create an IoT-based smart farming system using the Blynk application. The system has been successfully implemented to help monitor and control solenoid performance and water flow remotely. The results show that the IoT-based smart system has performed well and has the potential to increase efficiency, comfort and productivity in managing catfish ponds.

Agriculture and plantations are vital for sustaining a significant portion of Indonesia’s population. However, the agricultural sector faces considerable challenges, particularly due to its dependence on weather conditions, leading to fluctuations in production and market volatility. Effective water management is crucial for plant growth, and the specific water requirements of various crops, including melon plants, necessitate careful irrigation. The advancement of Internet of Things (IoT) technology presents substantial benefits for agriculture by optimizing plant growth, deterring pests, and enhancing irrigation systems. This research focuses on developing an automatic irrigation system specifically for melon farming, utilizing IoT technology. A capillary irrigation system controlled by water level sensors is implemented to ensure precise water management, reducing waste while improving plant health and yield. By enhancing agricultural productivity and promoting water sustainability, this system offers an efficient and reliable solution for automating irrigation, making it a suitable option for both household gardens and small-scale melon farms. The success of similar agricultural technologies, such as hydroponics and aquaponics, as demonstrated by Studio Tani Kalisuci in Gunung Kidul, highlights the potential of innovative farming practices in overcoming challenging environmental conditions.