Current Status and Future Opportunities for Grain Protein Prediction Using On- and Off-Combine Sensors: A Synthesis-Analysis of the Literature

The spatial information about crop grain protein concentration (GPC) can be an important layer (i.e., a map that can be utilized in a geographic information system) with uses from nutrient management to grain marketing. Recently, on- and off-combine harvester sensors have been developed for creating spatial GPC layers. The quality of these GPC layers, as measured by the coefficient of determination (R2) and the root mean squared error (RMSE) of the relationship between measured and predicted GPC, is affected by different sensing characteristics. The objectives of this synthesis analysis were to (i) contrast GPC prediction R2 and RMSE for different sensor types (on-combine, off-combine proximal and remote); (ii) contrast and discuss the best spatial, temporal, and spectral resolutions and features, and the best statistical approach for off-combine sensors; and (iii) review current technology limitations and provide future directions for spatial GPC research and application. On-combine sensors were more accurate than remote sensors in predicting GPC, yet with similar precision. The most optimal conditions for creating reliable GPC predictions from off-combine sensors were sensing near anthesis using multiple spectral features that include the blue and green bands, and that are analyzed by complex statistical approaches. We discussed sensor choice in regard to previously identified uses of a GPC layer, and further proposed new uses with remote sensors including same season fertilizer management for increased GPC, and in advance segregated harvest planning related to field prioritization and farm infrastructure. Limitations of the GPC literature were identified and future directions for GPC research were proposed as (i) performing GPC predictive studies on a larger variety of crops and water regimes; (ii) reporting proper GPC ground-truth calibrations; (iii) conducting proper model training, validation, and testing; (iv) reporting model fit metrics that express greater concordance with the ideal predictive model; and (v) implementing and benchmarking one or more uses for a GPC layer.

Farm supply and grain marketing businesses: A mixed methods investigation of past and intended future strategies

Based on a media analysis and a manager survey, we investigate past and intended future strategies of farm supply and grain marketing businesses in Germany. Alliances, diversification and organic growth were among the most popular past strategies and, according to the participants, will be in the future. Nevertheless, the trend towards divestment and fewer locations for grain collection will continue. Large, investor-owned firms prefer a combination of capital-intensive strategies, cooperatives and small and medium-sized enterprises plan to adopt fewer strategies. We outline several potential implications and areas for further research.

ANALISIS PEMASARAN PADI SAWAH DI KECAMATAN WAWOTOBI KABUPATEN KONAWE

ABSTRAK Tujuan dari penelitian yaitu untuk menganalisis saluran pemasaran gabah kering panen di Kelurahan Kulahi Kecamatan Wawotobi dan untuk menganalisis margin, keuntungan dan efisiensi pemasaran gabah kering panen di Kelurahan Kulahi Kecamatan Wawotobi. Penelitian dilaksanakan di Kelurahan Kulahi Kecamatan Wawotobi Kabupaten Konawe. Populasi dalam penelitian ini adalah semua petani padi sawah yang berjumlah 20 orang dan pedagang yang pemasaran gabah sebanyak 4 orang, penentuan sampel petani padi sawah dilakukan secara sensus. Pemasaran gabah terdapat satu jenis saluran pemasaran yaitu Produsen (Petani Padi) ke Pedagang Pengumpul Desa ke Penggilingan Padi, memiliki biaya pemasaran sebesar Rp 120/ Kg. Keuntungan pemasaran pedagang pengumpul desa sebesar Rp 280/Kg, dan memiliki marjin pemasaran sebesar Rp 400/Kg dan keuntungan pemasaran gabah yang sudah berbentuk beras yang dilakukan oleh penggilingan padi sebesar Rp 450.000/50Kg, dan memiliki marjin pemasaran sebesar Rp 90.000/Kg. Efisiensi pemasaran gabah mencapai 89%. Artinya besarnya persentase bagian harga yang diterima petani dan rendahnya biaya pemasaran yang terjadi pada lembaga pemasaran gabah di Kelurahan Kulahi Kecamatan Wawotobi Kabupaten Konawe. Kata Kunci : Pemasaran, Padi Sawah. ABSTRACT The purpose of this research is to analyze the marketing channels of harvested dry unhulled rice in Kulahi Village, Wawotobi District and to analyze the margins, profits and marketing efficiency of harvested dry grain in Kulahi Village, Wawotobi District. The research was conducted in Kulahi Village, Wawotobi District, Konawe Regency. The population in this study were all lowland rice farmers, amounting to 20 people and traders who marketed grain as many as 4 people. The sample determination of lowland rice farmers was carried out by census. There is one type of marketing channel for unhulled rice, namely Producers (Rice Farmers) to Village Collecting Traders to Rice Mills, having a marketing cost of IDR 120 / Kg. The marketing profit of the village collector traders is Rp. 280 / Kg, and has a marketing margin of Rp. 400 / Kg and the marketing profit of unhulled rice in the form of rice carried out by the rice mill is Rp. 450,000 / 50 kg, and has a marketing margin of Rp. 90,000 / kg. Grain marketing efficiency reaches 89%. This means that the large percentage of the price received by farmers and the low marketing costs that occur in grain marketing institutions in Kulahi Village, Wawotobi District, Konawe Regency. Keywords: Marketing, Rice Paddy

Convolutional Neural Networks for Image-Based Corn Kernel Detection and Counting

Precise in-season corn grain yield estimates enable farmers to make real-time accurate harvest and grain marketing decisions minimizing possible losses of profitability. A well developed corn ear can have up to 800 kernels, but manually counting the kernels on an ear of corn is labor-intensive, time consuming and prone to human error. From an algorithmic perspective, the detection of the kernels from a single corn ear image is challenging due to the large number of kernels at different angles and very small distance among the kernels. In this paper, we propose a kernel detection and counting method based on a sliding window approach. The proposed method detects and counts all corn kernels in a single corn ear image taken in uncontrolled lighting conditions. The sliding window approach uses a convolutional neural network (CNN) for kernel detection. Then, a non-maximum suppression (NMS) is applied to remove overlapping detections. Finally, windows that are classified as kernel are passed to another CNN regression model for finding the ( x , y ) coordinates of the center of kernel image patches. Our experiments indicate that the proposed method can successfully detect the corn kernels with a low detection error and is also able to detect kernels on a batch of corn ears positioned at different angles.