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Background and Objectives Grain quality is a complex trait in rice, compared with other staple crops as it is predominantly consumed as a whole grain. Although considered secondary to yield, to align with consumer preferences, breeders are increasingly interested in quality. At the early stages of a breeding program, grain quality‐related traits are often ignored as they are arduous and time‐consuming. Near‐infrared spectroscopy (NIRS) could be a suitable high‐throughput alternative to conventional wet chemistry and image processing‐related methods to be adopted for early screening. This study aims to quantify traits essential for rice breeders such as amylose, chalkiness, length, width, and the length/width ratio in rice samples with NIRS. We used conventional algorithms such as principal component analysis (PCA), partial least square regression (PLSR), multilayer perceptron (MLP), support vector classification (SVC), and linear discriminant analysis (LDA) to compare with the proposed convolutional neural network (CNN) for regression and classification. Findings Our results showed that the proposed CNN outperformed the conventional models in estimating all traits. Unlike conventional models, CNN models could be developed with raw spectra with minimal to no preprocessing, and along with the transfer‐learning capabilities, the time required for model development could be significantly reduced. Conclusion We recommend NIRS for quantitative estimation of amylose and chalkiness in rice and rather use classification/categorized estimation for other physical dimension‐related traits such as length and length/width ratio. Significance and Novelty We found NIRS to be an appropriate alternative to wet chemistry and image‐based methods for screening lines at the early stages of the breeding program. Estimation of physical parameters such as length and length/width ratio with NIRS is novel and appears reasonable for high‐throughput applications.

Introducing machine vision-based automation to the agricultural sector is essential to meet the food demand of a rapidly growing population. Furthermore, extensive labor and time are required in agriculture; hence, agriculture automation is a major concern and an emerging subject. Machine vision-based automation can improve productivity and quality by reducing errors and adding flexibility to the work process. Primarily, machine vision technology has been used to develop crop production systems by detecting diseases more efficiently. This review provides a comprehensive overview of machine vision applications for stress/disease detection on crops, leaves, fruits, and vegetables with an exploration of new technology trends as well as the future expectation in precision agriculture. In conclusion, research on the advanced machine vision system is expected to develop the overall agricultural management system and provide rich recommendations and insights into decision-making for farmers.

Agricultural biomass presents a promising feedstock, which may contribute to a transition to low carbon fuels. A significant amount of research has identified a number of challenges when combusting agricultural feedstock, related primarily to energy value, ash, emissions, corrosion and combustion characteristics. The mitigation of such challenges can be addressed more cost effectively when dealing with large or utility scale combustion. The costs associated with harvesting, conversion, transportation and ultimately, market development all create additional roadblocks for the creation of an agricultural biomass industry. Nova Scotia, an Eastern Canadian province, has significant land resources, however it is prone to wet spring and as yet does not have a supply chain established for such an industry. The main components of supply, processing and conversion and demand simply do not yet exist. This research addresses one aspect of this supply chain by attempting to develop a fuel suitable for a) existing markets (local residential wood and wood pellet stoves and b) a scale that will support industry engagement. The outcomes of this research have determined that such a venture is possible and presents empirical preprocessing conditions to achieve a competitive agricultural fuel.

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This paper describes the implementation of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm to minimize the total completion time of jobs called makespan in parallel line Job Shop Scheduling Problem (PJSSP). PJSSP is one of the scheduling methods in manufacturing industry for increasing plant utilization, reducing cycle time and to find the optimal allocation of jobs in multiple processing lines. Each job is assigned to a particular line and the job should be completed only in that assigned line. Also, the job should be processed in a particular order. PJSSP is always a challenging task in the combinatorial research and it requires a heuristic approach. Results show that the performance of PSO is superior to GA in order to find optimal solution with minimum makespan for PJSSP.