The EcoCropsAID dataset is a comprehensive collection of 5,400 aerial images captured between 2014 and 2018 using the Google Earth application. This dataset focuses on five key economic crops in Thailand: rice, sugarcane, cassava, rubber, and longan. The images were collected at various crop growth stages: early cultivation, growth, and harvest, resulting in significant variability within each category and similarities across different categories. These variations, coupled with differences in resolution, color, and contrast introduced by multiple remote imaging sensors, present substantial challenges for land use classification. The dataset is an interdisciplinary resource that spans multiple research domains, including remote sensing, geoinformatics, artificial intelligence, and computer vision. The unique features of the EcoCropsAID dataset offer opportunities for researchers to explore novel approaches, such as extracting spatial and temporal features, developing deep learning architectures, and implementing transformer-based models. The EcoCropsAID dataset provides a valuable platform for advancing research in land use classification, with implications for optimizing agricultural practices and enhancing sustainable development. This study explicitly investigates the use of deep learning algorithms to classify economic crop areas in northeastern Thailand, utilizing satellite imagery to address the challenges posed by diverse patterns and similarities across categories.

Disease detection in sugarcane, particularly the identification of asymptomatic infectious diseases such as Ratoon Stunting Disease (RSD), is critical for effective crop management. This study employed various machine learning techniques to detect the presence of RSD in different sugarcane varieties, using vegetation indices derived from freely available satellite-based spectral data. Our results show that the Support Vector Machine with a Radial Basis Function Kernel (SVM-RBF) was the most effective algorithm, achieving classification accuracy between 85.64% and 96.55%, depending on the variety. Gradient Boosting and Random Forest also demonstrated high performance achieving accuracy between 83.33% to 96.55%, while Logistic Regression and Quadratic Discriminant Analysis showed variable results across different varieties. The inclusion of sugarcane variety and vegetation indices was important in the detection of RSD. This agreed with what was identified in the current literature. Our study highlights the potential of satellite-based remote sensing as a cost-effective and efficient method for large-scale sugarcane disease detection alternative to traditional manual laboratory testing methods.

Research into large-scale crop monitoring has flourished due to increased accessibility to satellite imagery. This review delves into previously unexplored and under-explored areas in sugarcane health monitoring and disease/pest detection using satellite-based spectroscopy and Machine Learning (ML). It discusses key considerations in system development, including relevant satellites, vegetation indices, ML methods, factors influencing sugarcane reflectance, optimal growth conditions, common diseases, and traditional detection methods. Many studies highlight how factors like crop age, soil type, viewing angle, water content, recent weather patterns, and sugarcane variety can impact spectral reflectance, affecting the accuracy of health assessments via spectroscopy. However, these variables have not been fully considered in the literature. In addition, the current literature lacks comprehensive comparisons between ML techniques and vegetation indices. We address these gaps in this review. We discuss that, while current findings suggest the potential for an ML-driven satellite spectroscopy system for monitoring sugarcane health, further research is essential. This paper offers a comprehensive analysis of previous research to aid in unlocking this potential and advancing the development of an effective sugarcane health monitoring system using satellite technology.

This is the first time a highly efficient genomic selection method based on machine learning has been proposed for polyploid plants – in which cells have more than two complete sets of chromosomes. The methodology, published in Scientific Reports, improved the predictive power of machine learning by more than 50%. This means that this model is much more accurate than traditional breeding techniques. Machine learning is a branch of AI that involves computer statistics and optimisation with countless applications. Its main goal is to create algorithms that automatically extract patterns from datasets.

Scientists revealed the use of artificial intelligence to predict the sugarcane industry's performance efficiently.